{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6827013",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "environ['train_device'] = 'cuda:1' # training device: 'cpu' or 'cuda:X'\n",
    "environ['store_device'] = 'cuda:1' # Data storing device:  'cpu' or 'cuda:X'\n",
    "\n",
    "dataset_file_MC = '/data/kk4796/datasets/dataset_multicomp_fusion_train_val.pkl' \n",
    "test_dataset_file_MC = '/data/kk4796/datasets/dataset_multicomp_fusion_test.pkl' \n",
    "\n",
    "dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_train_val.pkl'\n",
    "test_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_test.pkl'\n",
    "\n",
    "\n",
    "%run utils_MultiLabel_vs_Classical.py # imports and defines some utils functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d418898d",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b781162e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering the schedules\n",
    "\n",
    "def filter_schedule_MC(schedule_str): # filter the LI schedules for MC, return true if we want the passed schedule to be dropped\n",
    "    \n",
    "    regex1 = \"^I\\(\\{[C0-9,]+\\},L[0-9]+,L[0-9]+\\)$\"\n",
    "    regex2 = \"F\\(\\{[C0-9,]+\\},L[0-9]+\\)I\\(\\{[C0-9,]+\\},L[0-9]+,L[0-9]+\\)$\"\n",
    "    if re.search(regex1, schedule_str) or re.search(regex2, schedule_str) : # drops all the schedules except the loop interchanges\n",
    "        return True\n",
    "    if schedule_str==\"\":\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def filter_schedule_SC(schedule_str): # filter the LI schedules for SC, return true if we want the passed schedule to be dropped\n",
    "    \n",
    "    regex = \"I\\(L[0-9]+,L[0-9]+\\)$\"\n",
    "    if re.search(regex, schedule_str): # drops all the schedules except the loop interchanges\n",
    "        return True\n",
    "    if schedule_str==\"\":\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "attachments": {
    "LI.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8c3e624c",
   "metadata": {},
   "source": [
    "![LI.png](attachment:LI.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d92b437",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1770/1770 [00:05<00:00, 323.41it/s] \n",
      "100%|██████████| 131466/131466 [02:58<00:00, 737.95it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 172\n",
      "Number of batches dropped due to too much memory accesses:5897\n",
      "Data loaded\n",
      "Sizes: (34, 138) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 196/196 [00:00<00:00, 910.14it/s]\n",
      "100%|██████████| 14607/14607 [00:18<00:00, 781.45it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 22\n",
      "Number of batches dropped due to too much memory accesses:642\n",
      "Data loaded\n",
      "Sizes: (22, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1770/1770 [00:01<00:00, 987.03it/s] \n",
      "100%|██████████| 131466/131466 [03:02<00:00, 721.08it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 172\n",
      "Number of batches dropped due to too much memory accesses:5897\n",
      "Data loaded\n",
      "Sizes: (34, 138) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 196/196 [00:00<00:00, 899.55it/s]\n",
      "100%|██████████| 14607/14607 [00:19<00:00, 733.70it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 22\n",
      "Number of batches dropped due to too much memory accesses:642\n",
      "Data loaded\n",
      "Sizes: (22, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Loading the data with classical representation ( 106 output)\n",
    "train_val_dataset_106, val_bl_106, val_indices_106, train_bl_106, train_indices_106 = load_merge_data_classical(dataset_file_MC, dataset_file_SC, 0.2, max_batch_size=832, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "test_dataset_106, test_bl_106, test_indices_106, _, _ = load_merge_data_classical(test_dataset_file_MC, test_dataset_file_SC, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "\n",
    "# Loading the data with multi_label representation ( 15 output)\n",
    "train_val_dataset_15, val_bl_15, val_indices_15, train_bl_15, train_indices_15 = load_merge_data_multiLabel(dataset_file_MC, dataset_file_SC, 0.2, max_batch_size=832, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "test_dataset_15, test_bl_15, test_indices_15, _, _ = load_merge_data_multiLabel(test_dataset_file_MC, test_dataset_file_SC, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06bbcb0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:1') 106\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:1') 15\n",
      "torch.Size([54, 2, 1272]) <BL0<BL1<BL2[CI0CI1]EL2>EL1>EL0>\n",
      "torch.Size([28, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3[CI1]EL3>EL1>EL0>\n",
      "torch.Size([4, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4[CI1]EL4>EL2>EL1>EL0>\n",
      "torch.Size([83, 2, 1272]) <BL0<BL1[CI0CI1]EL1>EL0>\n",
      "torch.Size([5, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4[CI1]EL4>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4<BL5[CI1]EL5>EL4>EL2>EL1>EL0>\n",
      "torch.Size([3, 2, 1272]) <BL0<BL1<BL2<BL3[CI0CI1]EL3>EL2>EL1>EL0>\n",
      "torch.Size([7, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3<BL4[CI1]EL4>EL3>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0CI1]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3><BL5[CI1]EL5>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2><BL5[CI1]EL5>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4<BL5<BL6[CI1]EL6>EL5>EL4>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1878, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1406, 1, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([304, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([139, 1, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([5, 1, 1272]) <BL0<BL1<BL2<BL3<BL4<BL5[CI0]EL5>EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([54, 2, 1272]) <BL0<BL1<BL2[CI0CI1]EL2>EL1>EL0>\n",
      "torch.Size([28, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3[CI1]EL3>EL1>EL0>\n",
      "torch.Size([4, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4[CI1]EL4>EL2>EL1>EL0>\n",
      "torch.Size([83, 2, 1272]) <BL0<BL1[CI0CI1]EL1>EL0>\n",
      "torch.Size([5, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4[CI1]EL4>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4<BL5[CI1]EL5>EL4>EL2>EL1>EL0>\n",
      "torch.Size([3, 2, 1272]) <BL0<BL1<BL2<BL3[CI0CI1]EL3>EL2>EL1>EL0>\n",
      "torch.Size([7, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3<BL4[CI1]EL4>EL3>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0CI1]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3><BL5[CI1]EL5>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2><BL5[CI1]EL5>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4<BL5<BL6[CI1]EL6>EL5>EL4>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1878, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1406, 1, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([304, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([139, 1, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([5, 1, 1272]) <BL0<BL1<BL2<BL3<BL4<BL5[CI0]EL5>EL4>EL3>EL2>EL1>EL0>\n"
     ]
    }
   ],
   "source": [
    "# Sanity check\n",
    "output106 = test_dataset_106.Y[1][0]\n",
    "output15 = test_dataset_15.Y[1][0]\n",
    "\n",
    "print(output106, len(output106))\n",
    "print(output15, len(output15))\n",
    "\n",
    "for i in range(len(test_dataset_106.X)):\n",
    "    print(test_dataset_106.X[i][1].size(), get_tree_footprint(test_dataset_106.X[i][0]) )  \n",
    "    #batch size, number of computations, size of each comps vector\n",
    "    \n",
    "for i in range(len(test_dataset_15.X)):\n",
    "    print(test_dataset_15.X[i][1].size(), get_tree_footprint(test_dataset_15.X[i][0]) )  \n",
    "    #batch size, number of computations, size of each comps vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568ea898",
   "metadata": {},
   "source": [
    "## Models Definition and Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9baef7bb",
   "metadata": {},
   "source": [
    "### Classical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0113ee63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000:  train Loss: 267.6103   val Loss: 151.2066   time: 5.51s   best: 151.2066\n",
      "Epoch 2/1000:  train Loss: 161.4211   val Loss: 142.8788   time: 1.26s   best: 142.8788\n",
      "Epoch 3/1000:  train Loss: 150.8268   val Loss: 139.3316   time: 1.26s   best: 139.3316\n",
      "Epoch 4/1000:  train Loss: 146.0207   val Loss: 137.3632   time: 1.30s   best: 137.3632\n",
      "Epoch 5/1000:  train Loss: 143.2608   val Loss: 135.7731   time: 1.18s   best: 135.7731\n",
      "Epoch 6/1000:  train Loss: 141.2041   val Loss: 134.8170   time: 1.18s   best: 134.8170\n",
      "Epoch 7/1000:  train Loss: 139.4659   val Loss: 134.0153   time: 1.18s   best: 134.0153\n",
      "Epoch 8/1000:  train Loss: 137.9872   val Loss: 133.0546   time: 1.17s   best: 133.0546\n",
      "Epoch 9/1000:  train Loss: 136.8343   val Loss: 131.9230   time: 1.17s   best: 131.9230\n",
      "Epoch 10/1000:  train Loss: 135.2046   val Loss: 130.5783   time: 1.17s   best: 130.5783\n",
      "Epoch 11/1000:  train Loss: 134.2300   val Loss: 129.2714   time: 1.43s   best: 129.2714\n",
      "Epoch 12/1000:  train Loss: 132.8679   val Loss: 128.3938   time: 1.33s   best: 128.3938\n",
      "Epoch 13/1000:  train Loss: 131.6601   val Loss: 127.8945   time: 1.33s   best: 127.8945\n",
      "Epoch 14/1000:  train Loss: 130.8717   val Loss: 127.2553   time: 1.38s   best: 127.2553\n",
      "Epoch 15/1000:  train Loss: 130.0861   val Loss: 126.9555   time: 1.39s   best: 126.9555\n",
      "Epoch 16/1000:  train Loss: 129.6102   val Loss: 126.7681   time: 1.37s   best: 126.7681\n",
      "Epoch 17/1000:  train Loss: 128.6960   val Loss: 126.3024   time: 1.38s   best: 126.3024\n",
      "Epoch 18/1000:  train Loss: 127.7934   val Loss: 126.6075   time: 1.35s   best: 126.3024\n",
      "Epoch 19/1000:  train Loss: 127.0993   val Loss: 126.5725   time: 1.33s   best: 126.3024\n",
      "Epoch 20/1000:  train Loss: 126.2634   val Loss: 125.1179   time: 1.33s   best: 125.1179\n",
      "Epoch 21/1000:  train Loss: 125.4930   val Loss: 123.9402   time: 1.30s   best: 123.9402\n",
      "Epoch 22/1000:  train Loss: 124.8681   val Loss: 123.1890   time: 1.32s   best: 123.1890\n",
      "Epoch 23/1000:  train Loss: 124.4207   val Loss: 122.8464   time: 1.30s   best: 122.8464\n",
      "Epoch 24/1000:  train Loss: 124.1495   val Loss: 121.6417   time: 1.28s   best: 121.6417\n",
      "Epoch 25/1000:  train Loss: 123.5219   val Loss: 122.8346   time: 1.28s   best: 121.6417\n",
      "Epoch 26/1000:  train Loss: 123.3548   val Loss: 122.2222   time: 1.29s   best: 121.6417\n",
      "Epoch 27/1000:  train Loss: 123.0302   val Loss: 123.9354   time: 1.29s   best: 121.6417\n",
      "Epoch 28/1000:  train Loss: 122.7065   val Loss: 121.0799   time: 1.29s   best: 121.0799\n",
      "Epoch 29/1000:  train Loss: 122.1084   val Loss: 121.4865   time: 1.29s   best: 121.0799\n",
      "Epoch 30/1000:  train Loss: 121.5245   val Loss: 120.3729   time: 1.29s   best: 120.3729\n",
      "Epoch 31/1000:  train Loss: 121.2107   val Loss: 122.2978   time: 1.28s   best: 120.3729\n",
      "Epoch 32/1000:  train Loss: 120.5603   val Loss: 120.2727   time: 1.29s   best: 120.2727\n",
      "Epoch 33/1000:  train Loss: 119.9209   val Loss: 119.0247   time: 1.32s   best: 119.0247\n",
      "Epoch 34/1000:  train Loss: 119.4179   val Loss: 119.1634   time: 1.34s   best: 119.0247\n",
      "Epoch 35/1000:  train Loss: 118.6866   val Loss: 118.1560   time: 1.33s   best: 118.1560\n",
      "Epoch 36/1000:  train Loss: 118.0516   val Loss: 116.1533   time: 1.33s   best: 116.1533\n",
      "Epoch 37/1000:  train Loss: 117.0501   val Loss: 113.7427   time: 1.32s   best: 113.7427\n",
      "Epoch 38/1000:  train Loss: 116.7942   val Loss: 113.1981   time: 1.32s   best: 113.1981\n",
      "Epoch 39/1000:  train Loss: 115.9934   val Loss: 112.6024   time: 1.32s   best: 112.6024\n",
      "Epoch 40/1000:  train Loss: 115.4714   val Loss: 111.4837   time: 1.29s   best: 111.4837\n",
      "Epoch 41/1000:  train Loss: 114.9035   val Loss: 110.8214   time: 1.29s   best: 110.8214\n",
      "Epoch 42/1000:  train Loss: 114.3924   val Loss: 110.7408   time: 1.29s   best: 110.7408\n",
      "Epoch 43/1000:  train Loss: 114.1242   val Loss: 110.4453   time: 1.29s   best: 110.4453\n",
      "Epoch 44/1000:  train Loss: 113.7306   val Loss: 110.1164   time: 1.29s   best: 110.1164\n",
      "Epoch 45/1000:  train Loss: 113.0908   val Loss: 110.4974   time: 1.20s   best: 110.1164\n",
      "Epoch 46/1000:  train Loss: 113.1377   val Loss: 110.6166   time: 1.29s   best: 110.1164\n",
      "Epoch 47/1000:  train Loss: 112.5020   val Loss: 109.5180   time: 1.29s   best: 109.5180\n",
      "Epoch 48/1000:  train Loss: 112.2734   val Loss: 109.2495   time: 1.32s   best: 109.2495\n",
      "Epoch 49/1000:  train Loss: 111.8338   val Loss: 108.9841   time: 1.33s   best: 108.9841\n",
      "Epoch 50/1000:  train Loss: 111.3027   val Loss: 108.4842   time: 1.33s   best: 108.4842\n",
      "Epoch 51/1000:  train Loss: 111.1136   val Loss: 109.0271   time: 1.32s   best: 108.4842\n",
      "Epoch 52/1000:  train Loss: 111.6912   val Loss: 108.7133   time: 1.33s   best: 108.4842\n",
      "Epoch 53/1000:  train Loss: 110.2277   val Loss: 109.0265   time: 1.32s   best: 108.4842\n",
      "Epoch 54/1000:  train Loss: 110.2177   val Loss: 107.8726   time: 1.32s   best: 107.8726\n",
      "Epoch 55/1000:  train Loss: 110.0028   val Loss: 107.8286   time: 1.30s   best: 107.8286\n",
      "Epoch 56/1000:  train Loss: 109.6319   val Loss: 109.1358   time: 1.30s   best: 107.8286\n",
      "Epoch 57/1000:  train Loss: 109.3395   val Loss: 107.7746   time: 1.29s   best: 107.7746\n",
      "Epoch 58/1000:  train Loss: 109.1488   val Loss: 107.2534   time: 1.30s   best: 107.2534\n",
      "Epoch 59/1000:  train Loss: 109.1880   val Loss: 108.0166   time: 1.30s   best: 107.2534\n",
      "Epoch 60/1000:  train Loss: 108.6822   val Loss: 107.5033   time: 1.29s   best: 107.2534\n",
      "Epoch 61/1000:  train Loss: 108.3524   val Loss: 107.0116   time: 1.24s   best: 107.0116\n",
      "Epoch 62/1000:  train Loss: 108.0168   val Loss: 107.6060   time: 1.17s   best: 107.0116\n",
      "Epoch 63/1000:  train Loss: 107.7752   val Loss: 106.1622   time: 1.18s   best: 106.1622\n",
      "Epoch 64/1000:  train Loss: 107.5949   val Loss: 106.1357   time: 1.19s   best: 106.1357\n",
      "Epoch 65/1000:  train Loss: 107.3757   val Loss: 105.7445   time: 1.20s   best: 105.7445\n",
      "Epoch 66/1000:  train Loss: 106.9306   val Loss: 105.9806   time: 1.21s   best: 105.7445\n",
      "Epoch 67/1000:  train Loss: 106.4613   val Loss: 105.4063   time: 1.21s   best: 105.4063\n",
      "Epoch 68/1000:  train Loss: 106.3007   val Loss: 104.6654   time: 1.21s   best: 104.6654\n",
      "Epoch 69/1000:  train Loss: 105.9034   val Loss: 105.3624   time: 1.20s   best: 104.6654\n",
      "Epoch 70/1000:  train Loss: 105.9307   val Loss: 104.0886   time: 1.20s   best: 104.0886\n",
      "Epoch 71/1000:  train Loss: 105.6468   val Loss: 104.8063   time: 1.18s   best: 104.0886\n",
      "Epoch 72/1000:  train Loss: 105.6153   val Loss: 104.1236   time: 1.18s   best: 104.0886\n",
      "Epoch 73/1000:  train Loss: 105.0932   val Loss: 104.4421   time: 1.18s   best: 104.0886\n",
      "Epoch 74/1000:  train Loss: 104.6297   val Loss: 103.1417   time: 1.24s   best: 103.1417\n",
      "Epoch 75/1000:  train Loss: 104.8657   val Loss: 104.3858   time: 1.19s   best: 103.1417\n",
      "Epoch 76/1000:  train Loss: 104.3186   val Loss: 102.6484   time: 1.18s   best: 102.6484\n",
      "Epoch 77/1000:  train Loss: 104.1793   val Loss: 103.9026   time: 1.18s   best: 102.6484\n",
      "Epoch 78/1000:  train Loss: 104.2936   val Loss: 104.2007   time: 1.17s   best: 102.6484\n",
      "Epoch 79/1000:  train Loss: 103.9845   val Loss: 102.9546   time: 1.28s   best: 102.6484\n",
      "Epoch 80/1000:  train Loss: 103.6423   val Loss: 102.6979   time: 1.30s   best: 102.6484\n",
      "Epoch 81/1000:  train Loss: 103.5638   val Loss: 102.5884   time: 1.32s   best: 102.5884\n",
      "Epoch 82/1000:  train Loss: 103.0472   val Loss: 101.6562   time: 1.31s   best: 101.6562\n",
      "Epoch 83/1000:  train Loss: 102.9566   val Loss: 102.9571   time: 1.31s   best: 101.6562\n",
      "Epoch 84/1000:  train Loss: 102.5945   val Loss: 102.3983   time: 1.33s   best: 101.6562\n",
      "Epoch 85/1000:  train Loss: 102.3996   val Loss: 101.7065   time: 1.36s   best: 101.6562\n",
      "Epoch 86/1000:  train Loss: 102.2902   val Loss: 101.4670   time: 1.30s   best: 101.4670\n",
      "Epoch 87/1000:  train Loss: 102.1222   val Loss: 100.7586   time: 1.29s   best: 100.7586\n",
      "Epoch 88/1000:  train Loss: 101.9621   val Loss: 101.3120   time: 1.28s   best: 100.7586\n",
      "Epoch 89/1000:  train Loss: 101.8432   val Loss: 101.4185   time: 1.28s   best: 100.7586\n",
      "Epoch 90/1000:  train Loss: 101.5202   val Loss: 101.0490   time: 1.28s   best: 100.7586\n",
      "Epoch 91/1000:  train Loss: 101.4634   val Loss: 101.1083   time: 1.28s   best: 100.7586\n",
      "Epoch 92/1000:  train Loss: 101.4564   val Loss: 100.0229   time: 1.28s   best: 100.0229\n",
      "Epoch 93/1000:  train Loss: 101.0011   val Loss: 101.4576   time: 1.28s   best: 100.0229\n",
      "Epoch 94/1000:  train Loss: 101.0151   val Loss: 100.6726   time: 1.28s   best: 100.0229\n",
      "Epoch 95/1000:  train Loss: 100.5313   val Loss: 100.0620   time: 1.28s   best: 100.0229\n",
      "Epoch 96/1000:  train Loss: 100.9557   val Loss: 101.1856   time: 1.28s   best: 100.0229\n",
      "Epoch 97/1000:  train Loss: 100.5175   val Loss: 99.3698   time: 1.27s   best: 99.3698\n",
      "Epoch 98/1000:  train Loss: 100.3666   val Loss: 103.4966   time: 1.30s   best: 99.3698\n",
      "Epoch 99/1000:  train Loss: 100.4861   val Loss: 98.7646   time: 1.31s   best: 98.7646\n",
      "Epoch 100/1000:  train Loss: 99.7438   val Loss: 99.2667   time: 1.31s   best: 98.7646\n",
      "Epoch 101/1000:  train Loss: 99.5516   val Loss: 98.9844   time: 1.31s   best: 98.7646\n"
     ]
    },
    {
     "data": {
      "image/png": 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IzMw84FEOERFpx8EMhBXKAL7Q6dpc6uAJ9/dMRKSja9fBHLZQSGjbRxERiRRRHczhDGVv2rSJwsJCCgsL6dy5M/n5+bu/r6qq2u+506dP59prr23yZxx55JEHULO9lZSUcMopp7TKtURE5NBqt5O/WoOFMZSdmZnJZ599BsBtt91GUlISP//5z3e/X1NTQyDQePMVFRVRVFTU5M+YPHlyyysmIiJRLcp7zKGh7Faa/jV+/HhuuOEGxo0bx4033si0adM48sgjGTZsGEceeSTz588HvtmDve2227j00kspLi6moKCAe+65Z/f1kpKSdh9fXFzM2WefTb9+/bjwwgt3D7+//vrr9OvXj6OOOoprr722RT3jp59+msGDBzNo0CBuvPFGAGpraxk/fjyDBg1i8ODB/PWvfwXgnnvuYcCAAQwZMoTzzjsv/MYSEZEDEhE95v95dTZzVm9r0Tm1tbX4/H52VNYQG/Dt3mmq3oC8FG49dWCL67JgwQLeffdd/H4/27ZtY9KkSQQCAd59911uuukmXnjhhb3OmTdvHu+//z7l5eX07duXH/3oR3s95ztr1ixmz55NXl4eY8aM4eOPP6aoqIgf/vCHTJo0iZ49e3L++ec3u56rV6/mxhtvZMaMGaSnp/Ptb3+bl19+ma5du7Jq1Sq++uorALZu3QrAH/7wB5YuXUpsbOzuMhEROfiiusdcrzWnfp1zzjn4/X4AysrKOOeccxg0aBDXX389s2fPbvSck08+mdjYWLKysujUqRPr1q3b65hRo0bRpUsXfD4fhYWFLFu2jHnz5lFQULD7meCWBPOnn35KcXEx2dnZBAIBLrzwQiZNmkRBQQFLlizhJz/5CW+++SYpKSkADBkyhAsvvJAnn3xyn0P0IiLS9iLiX+AD6dmWl5eTkJjE7NVl5KbGkZ0c1yp1SUxM3P31Lbfcwrhx43jppZdYtmwZxcXFjZ4TGxu7+2u/309NTU2zjglnNvm+zk1PT+fzzz/nrbfe4h//+AfPPvssjzzyCK+99hqTJk3ilVde4fbbb9/nfzJERKRtRXWPOZzJX81RVlZGfn4+AI899lirX79fv34sWbKEZcuWAfDMM880+9zDDz+cDz74gI0bN1JbW8vTTz/NMcccw8aNG6mrq+Oss87i9ttvZ+bMmdTV1bFy5UrGjRvHn/70J7Zu3UpFRUWrfx4REWlaRPSYD1Rbr/z1y1/+kksuuYS7776bY489ttWvHx8fz3333ceJJ55IVlYWo0aN2uexEydOpEuXLru/f+6557jzzjsZN24czjlOOukkTj/9dD7//HN+8IMfUFfnbexx5513Ultby0UXXURZWRnOOa6//nrS0tIoLy9v9c8kIiL7Z+1h8Y2ioiI3ffr0b5TNnTuX/v37H/A169d5/rK0jOzkGDqnxodbzUOioqKCpKQknHNcffXV9O7dm+uvv/6g/OyWrpVdL9zfu2hSP+NeDpzaMHxqw/C1RRua2Qzn3F7P1kb1UDZ4w9mH/r8eB+6hhx6isLCQgQMHUlZWxg9/+MNDXSUREWlDUT2UDaFgjuBkvv766w9aD1lERA696O8xY1orW0REIkb0B3OE95hFRKRjif5gJrLvMYuISMcS/cFsph6ziIhEjA4QzAe2iUVxcTFvvfXWN8r+9re/8eMf/3i/59Q/9nXSSSc1uub0bbfdxl133bXfn/3yyy8zZ86c3d//9re/5d13321B7Run7SFFRNq/6A9mDuwe8/nnn8+ECRO+UTZhwoRmr1f9+uuvk5aW1vIfzN7B/Lvf/Y7jjz/+gK4lIiKRJfqD2eyA7jGfffbZ/Pe//6WyshKAZcuWsXr1ao466ih+9KMfUVRUxMCBA7n11lsbPb9Hjx5s3LgRgDvuuIO+ffty/PHH794aErxnlEeOHMnQoUM566yz2LFjB5MnT+aVV17hF7/4BYWFhSxevJjx48fz/PPPA94KX8OGDWPw4MFceumlu+vXo0cPbr31VoYPH87gwYOZN29esz/rvraHvOqqq7Q9pIjIQRYZzzG/8StY+2WLTomvrQF/gPzqWq8g6P/mAZ0Hw3f+sM/zMzMzGTVqFG+++Sann346EyZM4Nxzz8XMuOOOO8jIyKC2tpbjjjuOL774giFDhjR6nRkzZjBhwgRmzZpFTU0Nw4cPZ8SIEQCceeaZXHHFFQDcfPPNPPzww/zkJz/htNNO45RTTuHss8/+xrV27drF+PHjmThxIn369OH73/8+999/P9dddx0AWVlZzJw5k/vuu4+77rqLf/3rX0220/62h1yzZo22hxQROciivsccjobD2Q2HsZ999lmGDx/OsGHDmD179jeGnff04Ycf8t3vfpeEhARSUlI47bTTdr/31VdfMXbsWAYPHsxTTz3V5I5O8+fPp2fPnvTp0weASy65hEmTJu1+/8wzzwRgxIgRuze+aMr+todcunSptocUETnIIuNf1v30bPdlZ2id57Ubt1Nb5+jVKanF1zjjjDO44YYbmDlzJjt37mT48OEsXbqUu+66i08//ZT09HTGjx/Prl279nsdq9/mag/jx4/n5ZdfZujQoTz22GOUlJTs9zpNLZRSv3XkvraWbMk109PTmTx5MpMnT25ye0gFtIhI64n6HrM3+evAnpdKSkqiuLiYSy+9dHdvedu2bSQmJpKamsq6det444039nuNo48+mpdeeomdO3dSXl7Oq6++uvu98vJycnNzqa6u5qmnntpdnpyc3OjOTv369WPZsmUsWrQIgH//+98cc8wxB/TZ6ml7SBGR9iXquzrhbmJx/vnnc+aZZ+4e0h46dCjDhg1j4MCBFBQUMGbMmP2eP3z4cM4991wKCwvp3r07Y8eO3f3e7bffzuGHH0737t0ZPHjw7jA+77zzuOKKK7jnnnt2T/oCiIuL49FHH+Wcc86hpqaGkSNHctVVV7Xo87Rke8hLLrlk93H72x5SRERaT5PbPprZI8ApwHrn3KBQ2TNA39AhacBW51yhmfUA5gL1U4+nOueaTI623PZxxaYd7KyupW/nlm9f2NFp28fwabu98KkNw6c2DN/B3PaxOT3mx4B7gSfqC5xz5za48F+AsgbHL3bOFR5wTVvZgS4wIiIicig0GczOuUmhnvBezJvV9D3g2FauV6s50AVGREREDoVw7zGPBdY55xY2KOtpZrOAbcDNzrkPGzvRzK4ErgTIycnZa0Zyamoq27Zt2+eM5qbU1tZSXl5OTU0ddXWu0clUsn/1bdgSzjl27drV5AzzjqKiokJtESa1YfjUhuE7mG0YbjCfDzzd4Ps1QDfn3CYzGwG8bGYDnXPb9jzROfcg8CB495j3HLtfunQpVVVVZGZmHlA4198fLa/dyfaaqgO6V9rRtfQes3OOTZs2kZaWxrBhw9qwZpFD9/bCpzYMn9owfAezDQ84mM0sAJwJjKgvc85VApWhr2eY2WKgDzC90YvsR5cuXSgtLWXDhg0HVL9du3YRFxdH2c5qtlfW4CuLP6DrdGT1bdgScXFx35j1LSIiLRNOj/l4YJ5zrrS+wMyygc3OuVozKwB6A0sO5OLBYJCePXsecOVKSkoYNmwYf3pzHg99WMrCO0464Gt1VPVtKCIiB0+TC4yY2dPAFKCvmZWa2WWht87jm8PYAEcDX5jZ58DzwFXOuc2tWeGWCvp9VNe6A15kRERE5GBqzqzsRvc5dM6Nb6TsBeCF8KvVeoJ+7/50TZ3b/bWIiEh7FfVLcgb93kesrq07xDURERFpWscJ5hoNZYuISPvXAYLZG76urlOPWURE2r8OEMwayhYRkcgR9cEcCAVzTa2GskVEpP2L+mCuH8quUo9ZREQiQAcIZvWYRUQkcnSYYNY9ZhERiQQdIJg1lC0iIpGjAwSzhrJFRCRydJhg1lC2iIhEgqgP5kD9AiMKZhERiQBRH8wxu3vMGsoWEZH2L+qDub7HXKMes4iIRICoD+b6e8yalS0iIpEg6oNZQ9kiIhJJoj6YNZQtIiKRJOqDWUPZIiISSaI+mNPig8QFfSzduP1QV0VERKRJUR/MAb+PIflpfLZy66GuioiISJOiPpgBhnVLY/aqbVTW1B7qqoiIiOxXhwnmqto65qzedqirIiIisl8dJJjTAZi1YuuhrYiIiEgTOkQw56TEkZcaxyzdZxYRkXauQwQzeL3mWSu2HOpqiIiI7FeHCebCrmmUbtnJhvLKQ10VERGRfeowwTysWxqAHpsSEZF2rcME86D8VAI+03C2iIi0ax0mmOOCfgbkpWhmtoiItGsdJpgBhnVN4/PSrdTWaacpERFpnzpWMHdLZ0dVLQvWlR/qqoiIiDSqgwVzGqCFRkREpP3qUMHcLSOBjMQYPlupCWAiItI+dahgNjMKu6apxywiIu1WdAazc96rEcO6prFwfQVlO6sPcqVERESa1mQwm9kjZrbezL5qUHabma0ys89Cr5MavPdrM1tkZvPN7IS2qvg+bVgAfx9CxuYZjb5dv6HFF6VbD2KlREREmqc5PebHgBMbKf+rc64w9HodwMwGAOcBA0Pn3Gdm/taqbLOkdYWK9WRsntXo20O6puIzKJm/4aBWS0REpDmaDGbn3CRgczOvdzowwTlX6ZxbCiwCRoVRv5YLxkP3MfsM5pS4IKcMyePpaSvYsr3qoFZNRESkKYEwzr3GzL4PTAd+5pzbAuQDUxscUxoq24uZXQlcCZCTk0NJSUkYVfmmLnSn186JTHnzWSrjOu31/qikOl6pquW2/yvhu71jWu3nRpuKiopW/X3piNSG4VMbhk9tGL6D2YYHGsz3A7cDLvTrX4BLAWvk2EZnYTnnHgQeBCgqKnLFxcUHWJVGrO8M9z3CEVnboajx636wZTrvL9nE/148huS4YOv97ChSUlJCq/6+dEBqw/CpDcOnNgzfwWzDA5qV7Zxb55yrdc7VAQ/x9XB1KdC1waFdgNXhVfEAZPdlV2wmLJ64z0OuGdeLbbtqeHLqioNYMRERkf07oGA2s9wG334XqJ+x/QpwnpnFmllPoDcwLbwqHgAzNmcMhyWToLam0UOGdk1jbO8sHv5oCbuqaw9yBUVERBrXnMelngamAH3NrNTMLgP+ZGZfmtkXwDjgegDn3GzgWWAO8CZwtXPukKTelvRhUFkGq6bv85hrxvViY0UVE6ap1ywiIu1Dk/eYnXPnN1L88H6OvwO4I5xKtYYt6UPBfLBoInQb3egxhxdkMrJHOg9MWsIFh3cnJhCd662IiEjkiNokqgkmQX7Rfu8zA1w9rhdrynbxtHrNIiLSDkRtMAPQ6zhYNRN27Psx7GP6ZHNUryzueG0uM5Y393FtERGRthHdwXzYcYCDJe/v8xAz494LhpGXFseVT8ygdMuOg1c/ERGRPUR3MOcPh7g0WPTefg9LS4jh4fEjqaqt4/LHp1NR2fhMbhERkbYW3cHs80NBsXefeR+7TdU7LDuJ+y4czsL1Ffz06VnU1u3/eBERkbYQ3cEM3n3m8jWwfk6Th47tnc1tpw5g4rz13PzylwpnERE56KI/mA87DjCY8VizDr/4iB5cM64XT09byY+enKHFR0RE5KCK/mBOzYdRV8C0h2DFJ8065ecn9OV/ThvIO3PXccFDU7ULlYiIHDTRH8wAx90KqV3glWugelezTrnkyB7cf+Fwvlq9jbPun8zKzZqtLSIiba9jBHNsEpz6d9i4ACb9qdmnnTgol6cuP5xN26s45f99xMS569qwkiIiIh0lmMGbBFZ4IXz0N1jzebNPG9kjg/9cPYb8tHgue3w6v399LtW1dW1XTxER6dA6TjADnHAHJGbBf66G2upmn9YjK5EXf3wkF43uxoOTlnDuA1NYtXVnG1ZUREQ6qo4VzPHpcPJfYO2X8N/rWxTOcUE//3vGYO45fxjz15Zzwl8n8e+py6nTI1UiItKKOlYwA/Q/FY7+Bcz6Nzx1Duzc2qLTTxuaxxs/PZqhXVO55eWv+N4DU1i0vrxt6ioiIh1OxwtmgGNvhtP/Acs+gkdOgC3LWnR6t8wEnrzscP589hAWrq/gpL9/xJ/fmkfZjub3wEVERBrTMYMZYNhFcPFLUL4WHjoOln3cotPNjHOKuvLuDcfwncGd+cf7iznqj+9x99vz2bpDzz2LiMiB6bjBDNBzLFw+EeLT4LGT4e2bm/2cc73s5Fj+ft4w3vjpWMb2yeKe9xZx1B/f5973FlKj2dsiItJCHTuYAbJ6wZUfQNEPYPL/gweLW/Q4Vb3+uSncd+EI3rxuLGN6ZXLX2ws4+59TWLZxe+vXWUREopaCGbwFSE75K1z4POzcAg8dC2/cCBsXtvhS/Tqn8MDFRdx7wTCWbtzOSfd8yDOfrsA1sbuViIgIKJi/qfe34MdTYMh58OnDcG8RPH4azHkFalu2R/MpQ/J487qxFHZN48YXvmT8o58yedFGBbSIiOyXgnlPCRlwxj/ghjlw7C2waTE8ezH8pS+89jNYPgXqmnfvODc1nicvO5ybT+7Pl6vKuOBfn/Ctv07i31OWUVHZsqAXEZGOQcG8L0md4Oifw3VfwPkTvIlis56CR0+Evw+Bib+DrSubvIzPZ1w+toDJvzqWu84ZSnzQzy3/mc2YP7zHv6cs057PIiLyDYFDXYF2z+eHvt/xXpXlMP8N+PI5+Oiv3qvPd2DkZVAwDnz7/n9OXNDP2SO6cNbwfGat3Mqf3pzHLf+Zzf9NW8n/nDaQUT0zDuKHEhGR9ko95paITYYh34MLn4Offg5jroOVn8CTZ8J9o+GLZ5u8F21mDO+WztNXjObeC4axdUcV33tgCtc+PYvlmzSDW0Sko1MwH6i0bnD8rd696DMf8nrWL14B/xgJs55sch1uM+OUIXlM/Nkx/OTYXrw1ey3H/eUDfvPSl6wta9mz1CIiEj0UzOEKxHq96Ks+hnOfhJgkb/eqf4zyhr2bmIWdEBPgZ9/uy6RfjuP8Ud145tOVHPPn9/ndq3OYs3qbZnGLiHQwCubW4vN5G2T8cJI3WcwXgKfPgyfPgg0Lmjw9JyWO288YxHs/K+bkIbk8PmUZJ93zIcff/QF/fWeBNsoQEekgFMytzcybKPajyXDCnVD6Kdx/BLx5k7d4SRO6ZSZw9/cKmXbTcfzvGYPITo7lnvcWcvzdkzjxb5O4972FWk1MRCSKaVZ2W/EH4Ygfw+Bz4L3fwdT74POnofjX3vKf/uB+T89MiuWi0d25aHR31m/bxetfruG/X6zhrrcXcNfbCxiQm0JRj3SGdEljSJdUDstOwu+zg/ThRESkrSiY21pSNpz2/2DUlfDWTfDGL+DTh+D426DPid6ksSZ0Solj/JiejB/Tk9Vbd/L6l2t4Z846XphRyhNTlgOQHBvglKG5nDeyG0O6pGKmkBYRiUQK5oOl82D4/ivehLC3b4YJF0ByLgw9Dwov8jbTaIa8tHguH1vA5WMLqK1zLNlQwRelZUxevImXZ63m6Wkr6Z+bwgWjunJaYT6p8fvvmYuISPuiYD6YzKDfSd6a3Ave9B6r+vgeb6GSvGHQ7QjIHwFdiiCtu3f8fvh9Ru+cZHrnJHPWiC7cetoA/vPZaiZMW8Et/5nNHa/P5aTBXi96ZI909aJFRCKAgvlQ8Ae9Gdz9T4XytfD5BC+opz/q3YsGSMrxVhPrdZz3a1J2k5dNiQty8ejuXDy6O1+WlvHM9BX8Z9ZqXpy5ioKsRL4zuDPH9c+hsEsaPt2PFhFplxTMh1pyZzjqOu9VWw3rZsOq6d5mGYvegS8meMd1GgiZh0F6D0jv7vWoE7MgIQsSMiEm4RuXHdwllcFdBvObkwbw2pdreH7GSv75wRL+8f5ispJiGNe3EycPyeWoXlkE/JqcLyLSXiiY2xN/EPIKvdfIy71drNZ8BovfgxVTYf1cr2ddW7X3uTHJkDMwdP4w6NQfdmwifssyzt6yjLMzSqlKrmHd9hpWb6thxexa7pl5FD9PHMxpQ/M4c3g+A/NSNNwtInKINRnMZvYIcAqw3jk3KFT2Z+BUoApYDPzAObfVzHoAc4H5odOnOueuaouKdwg+H+QP91716uqgYq23s9WOjbB9o/frtjWw9kuY+QR88s89rhOE1C7E+AJ0dbV09dUwKnYL5/AuHyScyvVTT+eRj5dSkJXISYNzOXlILv06Jx/czyoiIkDzesyPAfcCTzQoewf4tXOuxsz+CPwauDH03mLnXGFrVlIa8PkgJc97NaauFjYugA3zIDHbG/pOzt3rsSyrrID3f88xn9zP9LRP+Lj3L/nnhkHcV7KIe99fREFWIn2SKrG8DYzqkUF8TNOPdYmISPiaDGbn3KRQT7hh2dsNvp0KnN3K9ZID5fN7w9id+u//uNgkOPH3MOQcfK9cy9jPfs7Y7mPY+v1f8tq2nrzx5VreXbydNx+ZRozfR1GPdIp6ZDAoL4WB+ankpcZp2FtEpA1YczZJCAXzf+uHsvd471XgGefck6HjZgMLgG3Azc65D/dxzSuBKwFycnJGTJgw4UA/Q6MqKipISkpq1WtGK6urJW/1m3Rb8RyxVVvYnD6UZT0uYBldWFUVx1ebapmzqY7S8jrq/7QkBSE30UdmvJER5/3aNdlHQaqPgGZ876Y/h+FTG4ZPbRi+tmjDcePGzXDOFe1ZHlYwm9lvgCLgTOecM7NYIMk5t8nMRgAvAwOdc9v2d/2ioiI3ffr0Zn+Y5igpKaG4uLhVrxn1qnfCpw97z1Xv2Mi25F6kjLoQBpwOmYexs6qWuWu3MXtVGbNXb2P5ph2sLtvJmq27qKqtAyApNsDoggzG9s5mUH4q6QlB0hJiSI0Pfr1k6I7N8Oq1ULEeLnrB2+c6SunPYfjUhuFTG4avLdrQzBoN5gOelW1ml+BNCjvOhdLdOVcJVIa+nmFmi4E+QOumrrSNYDwceY23lveMx2DyozDxf7xXziDiux3B8MxeDM86DPoeBmmDwOejrs6xoaKSWSu28OHCjUxauIF3564HHOCFsRlkJsZyYvISflb+J1Jqt+BzdZT932XY9/5NamLsofzkIiLtxgEFs5mdiDfZ6xjn3I4G5dnAZudcrZkVAL2BJa1SUzl4YhLhiKuZWTmQ4sLDYO6rMO+/8MUzUNlg8COrL4y7CV//08hJiePEQbmcOKATfPk81e/dCTs2sSHrcJanFjEvfjidV7/Ft9f9i9V04uLK2zjcN49blj/J3b//MU/EnkvvTkkcUZDJEYdlMbx7GrGBJiacVe+CYFzbtoWIyEHWnMelngaKgSwzKwVuxZuFHQu8E5oAVP9Y1NHA78ysBqgFrnLObW6jusvBkNbV2yXriB+Dc97jWZsWwfo58MkD8NwlkDsUjr0FqrbD+7+HjfMJdh4MPceQt3QSeWve4Yj66w06i66n/I3nfIks31TBqjfLuWH583TqNoLnKoZw7/uLuOe9RcQGfIzskcHoggyOOCyTIV3SCNYvhFJTBa/d4P1H4bR7Yei5h6p1RERaXXNmZZ/fSPHD+zj2BeCFcCsl7ZSZtzRoUjZ0PwJGjIcvnoWSO+Gp0MT87H7wvSeg36neo13OwZalsKTEW6Ws/6lgRjzQLzcVLnoAHl3JRavv4KJLXqWiKsjy2dMpX/E569dXcM/iY7jr7S4kxPgp7JrGwLQqLi39Lblls6hKLSDmpSuhbCWM/VmTa4uLiEQCrfwlB87nh8LzYdBZ8NXzEIjzJoo1fGbaDDIKvFdjgvFw7pPwYDE8NI4kYCBAMAGc49TY11mXdzwvJZ/PnHUbGb/qNtLdVn5SfQ1vrRvJ3+Mf5jvv3c7yJfNJPfvvpCUlNP5zREQihIJZwheIgcILDvz81HwY/1+Y9xpk9YZOAyC9J+zaik29n86fPMCPVr8D/hhIyqDiu6/xw7h+jF2zjdfmd6V04f/jimXPUvLH+TyR/TMG9evH0X2yKeyapnXARSTiKJilfcju670aSsiAY3/jzRT/9F+w+jM48Q8kpeYzCBiUn8r3irpSW/coy98dydgpv+WIzZfzyKQTufS9U3GxqRR2S2NY1zSGdUunsGsa6Ykxh+LTiYg0m4JZ2r+4VO8e8j74fUb3b18NI0/G/94d/OjLZ7ks4QM+yDiHhRv97Fi6hQVsZxk1LM46li6Fx/PtgZ0pyNaCCyLS/iiYJXqk94CzHoIjryHm3f/hW4v/xbcAAlDnj6XWQXDrW8x5rzv3vXMiczK+xVH9uzC2dxYje2QQF9R64CJy6CmYJfrkDoWLX4Rtq72dteJS8AVi8VXvhC+epffk+7hr0wOUbX+GX06+ggcnDSM24GN0QSZnDs/npMG5Xz+aJSJykOlfH4leKXneo12B0KpiwXgYcQnBa6bC9/9Dak53/hn8C28fOY8LDu/G0o3b+emEzzjqj+9x73sL2VRReWjrLyIdknrM0vGYQUEx/OAN7IXL6TPzd9w6ehO33PA7Pli0mUc+Xspdby/gnvcW0Ss7iby0ODqnxpGbGs/hPTMY3i0dnzbqEJE2omCWjism0XuG+q2bYOo/8G1dzrgzH2Jcv8NZtL6cCdNWsnhDBaVbdjJ9+Ra27qgGID8tnlOG5nLqkDwG5qVo+0sRaVUKZunYfH74zh+9iWNv/homXADnT6BXp2RuPmXANw4t21nNxLnrePXz1Tz84VIe+GAJnZJjGd4tnRHd0xnePZ1B+SlNr/EtIrIfCmYRgNE/8raf/M/V8Nx4OPff4A9+45DU+CBnDu/CmcO7sHl7FW/PXssnSzczY/kW3py9FoDYgI/h3dI5vCCDw3tmUlXb9LaqIiINKZhF6g27yNuT+vWfw4tXwln/+ubyog1kJMZw3qhunDeqGwDry3cxc/kWPlm6mWlLN/P3iQtxbiF+g4FzP2JY1zSGd0+nX+cU8tLiSI4LNnpdEREFs0hDo66A6h3wzm+9Wdyn3ettxtGETsmhbS8H5QLesPf0ZZt56cPP2Yif52aU8viU5buPT44LkJ8WT9eMBPrmJNM7J4m+nZMpyEoiJqCHJUQ6MgWzyJ7G/NTrOZfcCQvegm6jodsR3it3KPib/muTGh/kuP45+NfFUFx8BDW1dcxfV87iDdtZvXUna7buZNXWnSzZUMF789ZTW+cNeccH/YwuyODoPtkc3SebgqzE3ZPLnHM4h2aEi0Q5BbNIY4650dtQY+E7sGIKzPuvVx6TDN2PhJ5He6+cQc3qUQf8PgbmpTIwL3Wv9ypralmyYTsL1pUzY/kWJi3YwPvzNwCQEOOnts5RU+eorXMkxPg5oiCTo/tkM7Z3Fj0bBLeIRAcFs0hjzLztLAed5X2/bY0X0Ms+hKWTYOFbXnl2f28d74HfbVZPujGxAT/9c1Pon5vC6YX5AKzYtINJCzewZMN2gn4j4DeCfh8bKyr5cOFGJs5b7/345FjyUuPITo4jJyWWvLR4+nVOZmBeKjkpsQptkQikYBZpjpRcGHSm9wIoWwWLJ8KU++DFy6Hk93DUDTDkXG8bzDB1y0zgoszu+3x/+abtTFq4kc9WbGV9+S5Wbt7BjOWb2RJ61hogMzGGAXkpDO+WTlGPdIZ1SycptvG/8lU1dXxRupV5a8v59oAcOqXEhf0ZROTAKJhFDkRqPgz/PhReBPNfg0l/hleugTd/5a0q1vtb0Otbbfbju2cmcnFmIheP/mZ4V1TWMHfNNmavKmPOmm18UVrGPe8t9O5NG/TJSSYnJY7MxBgyEmOIC/r5bOVWpi/fzK7qOgD++OY8fvWdfpw/spvuZ4scAgpmkXD4fND/VOh3Cix5H+a84t2XDt2THhNIhOnJEIyDQDwkZEJaN0jvDmndvYVNMgogqZM3fA7gHJSVwob5ULYSKsuhqgIqKyAuBUaMh+TOjVYnKTbAyB4ZjOyRsbusfFc1s1ZsZfryLXy1qoyNFZUsWl/B5u1V7KyupV/nZM4b2Y3RBZnkp8Vz5xtz+c1LX/HSzFXceeZgeuckt3EjikhDCmaR1mAGhx3rvZyDDfNg0busmz2FLjmZUL0LanbC9o2wpATK1wANFh+JSYKMnt5uWBsXeEG8p2Cid40P/wKFF3qzxzN6Nlm15Ljg7lnee6qprSOwx05aT11+OM/PKOWO1+fynb9/SFpCDGZej9tnRmp8kOzkWLKTYslOjqVbZgK9spPo1SmJzKTYFjaciOxJwSzS2sygU3/o1J9FVYPpUly89zE1lbB1JWxZBpuXwObF3q81lV7oZveF7H5ejzo22Qtun8875uN74LOnYObj0OdE79jULpDSxfs1NR/i0r7uge/HnqHsVd84p6gr4/p14uGPlobWCPce1aqtc2zdWc2G8kqWbNjOhopKqmrqdp+bnhCke2YiXdK9Z7S7pMeTkxxHRlLM7uHz2ED9TPM6amodibEBPbst0oCCWeRQCMRCVi/v1RIZBXDq37zHuabcC3NfhYVvQ13NN4+LSYKUfG/IPG8Y5BdBlyJIyPAmrq2YAiumwrqvIDEL0nt6ve+UfKhYD1uWkbVlKTeWr4U+J8DIy71NP/ZQV+dYs20Xi9Ztw33xHP0XPcSq8s7cVfF93pqdTnUzliSNCfgYnJ/KsK5pDOuWzsC8FLqkxzf6nwaRjkDBLBKJUnLhhDu8V12tF6Zlpd496W2rvPAtWwmbl8Kid8GFerXxGbBzs/d1TJL3HPaG+d5CKrVVX1/f/JDWFWJTvFXQPv67N3S+R0D7fEb+ts/J/+DXsHomdBpIztbZ/F/NT6k75mrWFf6E9ZUBNm+vYmNFJZu3V1FT5/D7jIDP8PuM1Vt2MGtlGU9MXc6/PloKQMBndM1IoHtmAsFdlSyPWUaPrER6ZiaSlxan0JaopmAWiXQ+vxfUKbnQdeTe71dWwOpZsGo6bFrkhXG30ZAz+Otnr+vqoHy1F+hJ2ZDa9etNPFZOg5I/fB3QWX2983xBqNkFyz+G5Fw445/e42LbN8C7t+Gb/Ddyv3yG3KLLoNvhUDDCC/W6Wq+3Pudlr8dfswu6jKSmXxHL4wfyuTuMRWXG8k07WLpxO4vX1/DO8tmA4zBbjc9gW1IBnVPiyEmJo1NKLBmJsWQkBEkPDZdnJ8fSKTmOtPigZpZLxFEwi0S72CToOdZ77YvPF7o/3WXv97qOgotf9AJ66n3eBLaaKqjb7oXsMb+CMdd+3ZNOzoHv3g9Fl8Lbv4H3/9crNz/kDISKdd4rEAe9jof4dCj9lMDCtzkMOAzz7pvnj4A+I5i3cDE9fGvxLf+QmJ3ewipTE0/lsZhLWLKplmnLNlO2sxrXyKh5wGd0To2jX+cUBuWnhFZfSyE3NU6Lr0i7pWAWkebpOsp7Nfv4kXDZ27BjM6yaASs/8cI9oycMOB16n+D9p6Hezq1er750hnf8gjfhs6foB5DYCQ4LLYO6cQGjp97P6KqpcMKdMPhsap23ccjm7VVsqqhkQ0UlG8orWV9eSemWncxZXcbEeet2h3dWUgyD81MZ3CWNgXkpZCXFkpYQJDXeewU1VC6HkIJZRNpWQoa34ErvJhZciU/zetC9jve+dw62LGPalI8ZddKF35xlPuRc+O913qprH92NPy6NDJ+fDJ+fXsFEyO7jLZda0B9Su8P2DezcXMra0qWs27iZKZU9eHtjgA8WbKBuj562GXTPSKBPTjL9OidzWKckgn4ftXWOutBGItnJsXRJjyc3NV4zyqXVKZhFpH0yg4ye7EhcvvejX7lD4LJ3YPoj3n3qulpvZnpNpbeu+cK39pqpHg/0DL1GA9fHplA7cDTrMkayslMxawP5lO2sZmN5JYs2VDB/bTmz5i1iIIv4tK4f24lvtIqdkmNJiAngMwj4fPh9RkF2IiO6pzOiezr9c1PUA5cWUTCLSGTy+b39s0ddsfd7NVXes+Hr53qLuSR28lZLS8711jJfOQ2WfYh/2cfkLXqbPO6ATgO9Vdx6j4H4T2Hnm7jyTzEcNfFZbC66gfKBF4A/yPptlZRu2cGmDWuIXz2V1b48SoM9qXVQVVvHrBVb+e8XawCIC/oYlJfKoPzU0PB5KkG/j3XbdrFu2y7Wb6skLSHIyB4ZdM9M0L1vUTCLSBQKxOxe5KVRad1g8Nne11tXekuozn0VPvgjfBAa284txIp/BZ0HE5h8L50+vIlOcx6F4l9x2PYNMO81WD4ZXK13fEq+98x3r2+BGdtWL2TzyvlUbVrOJ9v7cten43hssv8b1UhhOz/wv8l8svhF7dFkJ8cxqkcGvTolkRwXICUuSEp8ADNj565duLJV+LetpCYug+RuQ+mWkUC3jATiY755XYlsCmYR6djSusLoH3mvivVQ+qm3KEtK3tfH9D0J5r8O79wKL1zmlWX3h6Ou8+6Jb1rkPQv++TPe8DqQAqTEpkBSDn3KPuaijHdYO+rXTIk7Gl9dFUPXPEe32ffjr9wKwDXdVnB/0k/4aOVWXvvS6213ZhPXBl7kKN9X5NkmAvb1KmuvfTyKq2rOZanLpVNyLD2yEinISqRnViJ5afHEB/3EBn3EBf2srqijrs7p0bF6zkFtdavsBNcWFMwiIvWSOkG/k/cuN/PKe3/bW+s8owAyD/v6/e5HeruN1VR6we6P9Y5JyPDOXfIB9tZvyH3nx5yZX+Q9Lla2Eg47Do67BRZNpMf7d/DHjIUw/nFqU/KpnvRXYj59AFwd5d2/RUXGYfgyuhPM6olbPpUTp93LdwIzmNP5DF5JOofPymp4Z846Nm2v2rv+wJ2fvk1hN291tUF5KSTFBYgP+okP+khx5eTk5OHvKPfCJ90Fn/wTrvnU+z1qZxTMIiLN5Q/uf3Z5IBZ6HLV3ecEx8MMP4POnoeSP3n8ATr/X2yIUvB56t9Hw/GXwr+PwB+Px79wKQ74H435Davoee3P3PQ6OvAI++BODZjzKoDUvgC8AybnUdM5lZ0wm1YFEqv0JVPnimb2hhhmdzuCj1VXc+97C3TPRB9kSbgk+ST/fPNa6DObGDGJt+gh25Y3G36kvmUlxZCZ5i7YkxQZIjAmQEOuP7MlsZaXeRjA1O71wHnfToa7RXhTMIiIHg88Pwy7yXo3pcRRc9RG8eq03o/zYW7zZ5/uS1AlOvguO+DEsfg+2eSu3BbatIrliKVRt93Ypq9pO19oqTtz0BAw4jR0nXcKy2k5kT/sT2UtepDI2g6+6XoltXkph2UzS10+C9bCyLpuJdcN4pm4YU+sGUEVw94+O8ftITQiSnhAkLT6GtITg7ufA0xJiSIkPkhwb8MI8NkByXIDOqd4+4Id8ctvE270larsdAVP/CUdcDXGph7ZOe1Awi4i0F0nZcP7TLTsno8B77ce0155glG82fP40CV+9wADM6/2PuY7YsTcwqD6YQs+O1y4uodO8N/n+8hLG17xNrS+W7fG5VMRksy2YxeZAJ+bGDmWmDWTjTsfyTTv4orSarTurqKuupL8tJ8PKSaCSBNtFPJVscGmsDnaDjALys1JJjQ8S4/cR9PsIBnz4Q4FtBgbkpMYxKC+VvjlJxC19B3Ztg74nNj9Endv7MbvVs+CLCTDmOhh4BjxYDNMehKN/0ZIWb3NNBrOZPQKcAqx3zg0KlWUAzwA9gGXA95xzW0Lv/Rq4DKgFrnXOvdUmNRcRkWbZkdgNir8Px93qrVG+fo63IUl6j28eGHp23J/RE//IH0D1Tlj6If6lH5BSVkpK+Rryts2Gje9wZN3jXBab4k1+GzkOtq6A5VNwq6ZjNbv2WZfaLT5Wb+3MDhdLgCqCroYANcyq68UDtafypSvYvUJbd1vLHcFHOcr3JQDVBPkifiSfxB/Nssyj6ds9j8KuqQzM8x5BW7axnC0zXqLLV/cRW72N6aP+Sm7/I+iTk0yM3+CtmyEhE8be4AV872/DlPvg8B99cxW6Q6w5PebHgHuBJxqU/QqY6Jz7g5n9KvT9jWY2ADgPGAjkAe+aWR/n6p8nEBGRQyYmAQovaP7xwXjo823v1VDVDlj6gffI2II3YfaL3lrouUOwosu8++Up+d766TEJEIj3dj3buAD/hvl03bTQmxXtD3oT5YC8BW9xcuUnUDAOd+S1bFs0maRpf6fGgjyecg1f1fVgbNVHjKn8kBE7J1Oz+c/Mmn8YH9YN4k9uMPn+LVzJSxT5VrLc5VBHDUd/eBE3v/8DXmYc5yZ/yf/u+ognMq5lxstLiAv4yd51Oj/f+Tb/vve3vJ78PYr7ZnPioM50z9x7i9ODqclgds5NMrMeexSfDhSHvn4cKAFuDJVPcM5VAkvNbBEwCpjSSvUVEZFDLSYB+n7He9XVwcb53gYoscn7PicpG/IK9/3+rjKY/ihM+Qf25HdJBRh4Jv4Tfs8lKbmhg67wft7KqQQWvkPhwvcoWvcS1/Ei4O06tnLk3+l8xPkEq8rZNWE8fy59kAs7rSd3y3RWBbrxAsezZcVWdlXXkhTbheOChZy2/QVe9H+HO9/YxJ1vzKNf52SO7deJvLR4MkM7lq2uqKOyppbYQNs/M26usS1Z9jzIC+b/NhjK3uqcS2vw/hbnXLqZ3QtMdc49GSp/GHjDOfd8I9e8ErgSICcnZ8SECRNa4eN8raKigqSk9jM0EYnUhuFTG4ZPbRi+SGpDX20V2RsmUxmbwdb0/Ux+CwlUV5C29UucBdiUOQKswYxxV0vPpU/RfcULAHwx+BY2ZxZ94/zUrbMZ9tlNLOx1ObNTi9lUOp/ApnkkVa5lvUuj1GWzymVR6rL4/qjudEuPa7XPOm7cuBnOuaI9y1t78ldj0+0aTX7n3IPAgwBFRUWuuLi4VStSUlJCa1+zo1Ebhk9tGD61Yfgirw2/3fQh33DKvt8adxzMPwfWz2XIUdfvPSGMYtjyGr2XPE7vuofxIstwabmwfQNWV737yO3D3iWxRyN7nreyAw3mdWaW65xbY2a5wPpQeSnQtcFxXYDV4VRQREQkLPXD7vtywu/ho79CziDoUgT5w7G4VG9zlPK1ULaSOVPeYkBuv4NS3QMN5leAS4A/hH79T4Py/zOzu/Emf/UGpoVbSRERkTaTVwjfe3zvcp8fUvMhNZ/1S3YxYH/30FtRcx6XehpvoleWmZUCt+IF8rNmdhmwAjgHwDk328yeBeYANcDVmpEtIiLSfM2ZlX3+Pt46bh/H3wHcEU6lREREOqoIXvBUREQk+iiYRURE2hEFs4iISDuiYBYREWlHFMwiIiLtiIJZRESkHVEwi4iItCMKZhERkXZEwSwiItKOKJhFRETaEQWziIhIO2LONbpd8sGthNkGYHkrXzYL2NjK1+xo1IbhUxuGT20YPrVh+NqiDbs757L3LGwXwdwWzGy6c67oUNcjkqkNw6c2DJ/aMHxqw/AdzDbUULaIiEg7omAWERFpR6I5mB881BWIAmrD8KkNw6c2DJ/aMHwHrQ2j9h6ziIhIJIrmHrOIiEjEUTCLiIi0I1EXzGZ2opnNN7NFZvarQ12fSGBmXc3sfTOba2azzeynofIMM3vHzBaGfk0/1HVt78zMb2azzOy/oe/Vhi1gZmlm9ryZzQv9eTxCbdhyZnZ96O/yV2b2tJnFqR33z8weMbP1ZvZVg7J9tpmZ/TqUM/PN7ITWrEtUBbOZ+YF/AN8BBgDnm9mAQ1uriFAD/Mw51x8YDVwdardfAROdc72BiaHvZf9+Csxt8L3asGX+DrzpnOsHDMVrS7VhC5hZPnAtUOScGwT4gfNQOzblMeDEPcoabbPQv4/nAQND59wXyp9WEVXBDIwCFjnnljjnqoAJwOmHuE7tnnNujXNuZujrcrx/DPPx2u7x0GGPA2cckgpGCDPrApwM/KtBsdqwmcwsBTgaeBjAOVflnNuK2vBABIB4MwsACcBq1I775ZybBGzeo3hfbXY6MME5V+mcWwoswsufVhFtwZwPrGzwfWmoTJrJzHoAw4BPgBzn3BrwwhvodAirFgn+BvwSqGtQpjZsvgJgA/Bo6HbAv8wsEbVhizjnVgF3ASuANUCZc+5t1I4HYl9t1qZZE23BbI2U6XmwZjKzJOAF4Drn3LZDXZ9IYmanAOudczMOdV0iWAAYDtzvnBsGbEfDrS0Wug96OtATyAMSzeyiQ1urqNOmWRNtwVwKdG3wfRe8IRxpgpkF8UL5Kefci6HidWaWG3o/F1h/qOoXAcYAp5nZMrxbKMea2ZOoDVuiFCh1zn0S+v55vKBWG7bM8cBS59wG51w18CJwJGrHA7GvNmvTrIm2YP4U6G1mPc0sBu/m/CuHuE7tnpkZ3n29uc65uxu89QpwSejrS4D/HOy6RQrn3K+dc12ccz3w/ty955y7CLVhsznn1gIrzaxvqOg4YA5qw5ZaAYw2s4TQ3+3j8OaNqB1bbl9t9gpwnpnFmllPoDcwrbV+aNSt/GVmJ+Hd6/MDjzjn7ji0NWr/zOwo4EPgS76+P3oT3n3mZ4FueH/Zz3HO7Tk5QvZgZsXAz51zp5hZJmrDZjOzQrzJczHAEuAHeB0ItWELmNn/AOfiPXExC7gcSELtuE9m9jRQjLe94zrgVuBl9tFmZvYb4FK8Nr7OOfdGq9Ul2oJZREQkkkXbULaIiEhEUzCLiIi0IwpmERGRdkTBLCIi0o4omEVERNoRBbOI7JeZFdfvliUibU/BLCIi0o4omEWihJldZGbTzOwzM3sgtDd0hZn9xcxmmtlEM8sOHVtoZlPN7Asze6l+n1kz62Vm75rZ56FzDgtdPqnBPslPhVaUEpE2oGAWiQJm1h9vpacxzrlCoBa4EEgEZjrnhgMf4K1mBPAEcKNzbgjeim/15U8B/3DODcVbX3lNqHwYcB3ePucFeGuDi0gbCBzqCohIqzgOGAF8GurMxuMtuF8HPBM65kngRTNLBdKccx+Eyh8HnjOzZCDfOfcSgHNuF0DoetOcc6Wh7z8DegAftfmnEumAFMwi0cGAx51zv/5Godktexy3vzV49zc8Xdng61r0b4dIm9FQtkh0mAicbWadAMwsw8y64/0dPzt0zAXAR865MmCLmY0NlV8MfBDag7vUzM4IXSPWzBIO5ocQEf2vVyQqOOfmmNnNwNtm5gOqgauB7cBAM5sBlOHdhwZvC7t/hoK3fhcn8EL6ATP7Xega5xzEjyEiaHcpkahmZhXOuaRDXQ8RaT4NZYuIiLQj6jGLiIi0I+oxi4iItCMKZhERkXZEwSwiItKOKJhFRETaEQWziIhIO/L/ATae/b0fDm5WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/1000:  train Loss: 99.5102   val Loss: 99.2279   time: 1.31s   best: 98.7646\n",
      "Epoch 103/1000:  train Loss: 99.4702   val Loss: 98.4933   time: 1.31s   best: 98.4933\n",
      "Epoch 104/1000:  train Loss: 99.2493   val Loss: 98.8074   time: 1.30s   best: 98.4933\n",
      "Epoch 105/1000:  train Loss: 99.3333   val Loss: 98.7713   time: 1.29s   best: 98.4933\n",
      "Epoch 106/1000:  train Loss: 99.5133   val Loss: 100.8073   time: 1.28s   best: 98.4933\n",
      "Epoch 107/1000:  train Loss: 99.0394   val Loss: 97.7813   time: 1.28s   best: 97.7813\n",
      "Epoch 108/1000:  train Loss: 98.4903   val Loss: 97.4024   time: 1.28s   best: 97.4024\n",
      "Epoch 109/1000:  train Loss: 98.4685   val Loss: 99.2688   time: 1.28s   best: 97.4024\n",
      "Epoch 110/1000:  train Loss: 98.4330   val Loss: 97.6614   time: 1.28s   best: 97.4024\n",
      "Epoch 111/1000:  train Loss: 98.3991   val Loss: 97.1191   time: 1.27s   best: 97.1191\n",
      "Epoch 112/1000:  train Loss: 98.0448   val Loss: 97.3459   time: 1.27s   best: 97.1191\n",
      "Epoch 113/1000:  train Loss: 97.9531   val Loss: 97.9200   time: 1.28s   best: 97.1191\n",
      "Epoch 114/1000:  train Loss: 97.9422   val Loss: 99.0033   time: 1.31s   best: 97.1191\n",
      "Epoch 115/1000:  train Loss: 97.8933   val Loss: 97.6748   time: 1.31s   best: 97.1191\n",
      "Epoch 116/1000:  train Loss: 97.6973   val Loss: 97.5111   time: 1.32s   best: 97.1191\n",
      "Epoch 117/1000:  train Loss: 97.5470   val Loss: 97.3910   time: 1.31s   best: 97.1191\n",
      "Epoch 118/1000:  train Loss: 97.4397   val Loss: 97.8638   time: 1.31s   best: 97.1191\n",
      "Epoch 119/1000:  train Loss: 97.5109   val Loss: 96.0765   time: 1.30s   best: 96.0765\n",
      "Epoch 120/1000:  train Loss: 96.9444   val Loss: 95.7492   time: 1.29s   best: 95.7492\n",
      "Epoch 121/1000:  train Loss: 96.8916   val Loss: 96.3942   time: 1.28s   best: 95.7492\n",
      "Epoch 122/1000:  train Loss: 97.0739   val Loss: 96.5876   time: 1.28s   best: 95.7492\n",
      "Epoch 123/1000:  train Loss: 96.5905   val Loss: 95.9435   time: 1.28s   best: 95.7492\n",
      "Epoch 124/1000:  train Loss: 96.4438   val Loss: 96.2426   time: 1.28s   best: 95.7492\n",
      "Epoch 125/1000:  train Loss: 96.3017   val Loss: 95.2297   time: 1.28s   best: 95.2297\n",
      "Epoch 126/1000:  train Loss: 95.9942   val Loss: 97.4227   time: 1.27s   best: 95.2297\n",
      "Epoch 127/1000:  train Loss: 96.4653   val Loss: 95.9453   time: 1.27s   best: 95.2297\n",
      "Epoch 128/1000:  train Loss: 97.2195   val Loss: 95.0670   time: 1.27s   best: 95.0670\n",
      "Epoch 129/1000:  train Loss: 95.8995   val Loss: 95.3031   time: 1.29s   best: 95.0670\n",
      "Epoch 130/1000:  train Loss: 95.4739   val Loss: 94.2236   time: 1.32s   best: 94.2236\n",
      "Epoch 131/1000:  train Loss: 95.9438   val Loss: 94.2656   time: 1.31s   best: 94.2236\n",
      "Epoch 132/1000:  train Loss: 95.3847   val Loss: 94.3670   time: 1.31s   best: 94.2236\n",
      "Epoch 133/1000:  train Loss: 95.1139   val Loss: 95.0502   time: 1.31s   best: 94.2236\n",
      "Epoch 134/1000:  train Loss: 94.7575   val Loss: 93.7776   time: 1.31s   best: 93.7776\n",
      "Epoch 135/1000:  train Loss: 94.6541   val Loss: 94.7284   time: 1.31s   best: 93.7776\n",
      "Epoch 136/1000:  train Loss: 97.0467   val Loss: 94.2297   time: 1.29s   best: 93.7776\n",
      "Epoch 137/1000:  train Loss: 94.1826   val Loss: 94.0245   time: 1.28s   best: 93.7776\n",
      "Epoch 138/1000:  train Loss: 94.0455   val Loss: 93.9828   time: 1.28s   best: 93.7776\n",
      "Epoch 139/1000:  train Loss: 93.6577   val Loss: 94.1792   time: 1.28s   best: 93.7776\n",
      "Epoch 140/1000:  train Loss: 93.4587   val Loss: 92.8896   time: 1.28s   best: 92.8896\n",
      "Epoch 141/1000:  train Loss: 93.0019   val Loss: 92.3228   time: 1.27s   best: 92.3228\n",
      "Epoch 142/1000:  train Loss: 92.8265   val Loss: 92.0679   time: 1.27s   best: 92.0679\n",
      "Epoch 143/1000:  train Loss: 92.8898   val Loss: 93.8684   time: 1.27s   best: 92.0679\n",
      "Epoch 144/1000:  train Loss: 93.5037   val Loss: 91.1988   time: 1.28s   best: 91.1988\n",
      "Epoch 145/1000:  train Loss: 91.8428   val Loss: 90.5467   time: 1.31s   best: 90.5467\n",
      "Epoch 146/1000:  train Loss: 91.5942   val Loss: 89.4571   time: 1.31s   best: 89.4571\n",
      "Epoch 147/1000:  train Loss: 90.9837   val Loss: 88.7372   time: 1.31s   best: 88.7372\n",
      "Epoch 148/1000:  train Loss: 90.7655   val Loss: 88.5333   time: 1.26s   best: 88.5333\n",
      "Epoch 149/1000:  train Loss: 90.4005   val Loss: 87.6964   time: 1.22s   best: 87.6964\n",
      "Epoch 150/1000:  train Loss: 90.0277   val Loss: 86.9381   time: 1.21s   best: 86.9381\n",
      "Epoch 151/1000:  train Loss: 90.7671   val Loss: 86.8448   time: 1.20s   best: 86.8448\n",
      "Epoch 152/1000:  train Loss: 88.9185   val Loss: 86.6476   time: 1.18s   best: 86.6476\n",
      "Epoch 153/1000:  train Loss: 88.6999   val Loss: 85.7301   time: 1.19s   best: 85.7301\n",
      "Epoch 154/1000:  train Loss: 88.1674   val Loss: 85.8542   time: 1.23s   best: 85.7301\n",
      "Epoch 155/1000:  train Loss: 88.3384   val Loss: 85.5684   time: 1.19s   best: 85.5684\n",
      "Epoch 156/1000:  train Loss: 87.4171   val Loss: 84.7931   time: 1.18s   best: 84.7931\n",
      "Epoch 157/1000:  train Loss: 86.9660   val Loss: 84.5998   time: 1.18s   best: 84.5998\n",
      "Epoch 158/1000:  train Loss: 86.4965   val Loss: 83.8021   time: 1.17s   best: 83.8021\n",
      "Epoch 159/1000:  train Loss: 85.9011   val Loss: 84.2321   time: 1.17s   best: 83.8021\n",
      "Epoch 160/1000:  train Loss: 86.1206   val Loss: 83.7839   time: 1.18s   best: 83.7839\n",
      "Epoch 161/1000:  train Loss: 85.8162   val Loss: 82.5696   time: 1.19s   best: 82.5696\n",
      "Epoch 162/1000:  train Loss: 84.3558   val Loss: 82.0697   time: 1.21s   best: 82.0697\n",
      "Epoch 163/1000:  train Loss: 84.4140   val Loss: 82.1793   time: 1.21s   best: 82.0697\n",
      "Epoch 164/1000:  train Loss: 85.2781   val Loss: 82.7646   time: 1.21s   best: 82.0697\n",
      "Epoch 165/1000:  train Loss: 84.3484   val Loss: 81.6655   time: 1.21s   best: 81.6655\n",
      "Epoch 166/1000:  train Loss: 83.6459   val Loss: 80.9728   time: 1.21s   best: 80.9728\n",
      "Epoch 167/1000:  train Loss: 82.8764   val Loss: 79.9927   time: 1.21s   best: 79.9927\n",
      "Epoch 168/1000:  train Loss: 82.2535   val Loss: 79.9309   time: 1.19s   best: 79.9309\n",
      "Epoch 169/1000:  train Loss: 82.0511   val Loss: 79.7704   time: 1.18s   best: 79.7704\n",
      "Epoch 170/1000:  train Loss: 81.9023   val Loss: 79.6353   time: 1.18s   best: 79.6353\n",
      "Epoch 171/1000:  train Loss: 81.8184   val Loss: 79.1851   time: 1.18s   best: 79.1851\n",
      "Epoch 172/1000:  train Loss: 82.0380   val Loss: 78.6726   time: 1.18s   best: 78.6726\n",
      "Epoch 173/1000:  train Loss: 81.2587   val Loss: 78.0475   time: 1.18s   best: 78.0475\n",
      "Epoch 174/1000:  train Loss: 81.1166   val Loss: 78.6614   time: 1.17s   best: 78.0475\n",
      "Epoch 175/1000:  train Loss: 80.5186   val Loss: 78.6639   time: 1.17s   best: 78.0475\n",
      "Epoch 176/1000:  train Loss: 80.4730   val Loss: 78.1401   time: 1.17s   best: 78.0475\n",
      "Epoch 177/1000:  train Loss: 80.0766   val Loss: 77.7357   time: 1.19s   best: 77.7357\n",
      "Epoch 178/1000:  train Loss: 80.9418   val Loss: 77.9664   time: 1.21s   best: 77.7357\n",
      "Epoch 179/1000:  train Loss: 80.1288   val Loss: 77.2152   time: 1.21s   best: 77.2152\n",
      "Epoch 180/1000:  train Loss: 80.2312   val Loss: 77.7628   time: 1.21s   best: 77.2152\n",
      "Epoch 181/1000:  train Loss: 78.9758   val Loss: 76.6430   time: 1.21s   best: 76.6430\n",
      "Epoch 182/1000:  train Loss: 79.2313   val Loss: 76.4332   time: 1.21s   best: 76.4332\n",
      "Epoch 183/1000:  train Loss: 78.5158   val Loss: 77.1926   time: 1.20s   best: 76.4332\n",
      "Epoch 184/1000:  train Loss: 78.8957   val Loss: 76.1506   time: 1.19s   best: 76.1506\n",
      "Epoch 185/1000:  train Loss: 78.5817   val Loss: 75.9244   time: 1.18s   best: 75.9244\n",
      "Epoch 186/1000:  train Loss: 78.2308   val Loss: 76.0874   time: 1.18s   best: 75.9244\n",
      "Epoch 187/1000:  train Loss: 78.1070   val Loss: 75.6894   time: 1.18s   best: 75.6894\n",
      "Epoch 188/1000:  train Loss: 77.8799   val Loss: 75.6705   time: 1.18s   best: 75.6705\n",
      "Epoch 189/1000:  train Loss: 77.4684   val Loss: 75.3297   time: 1.18s   best: 75.3297\n",
      "Epoch 190/1000:  train Loss: 77.4158   val Loss: 75.4343   time: 1.18s   best: 75.3297\n",
      "Epoch 191/1000:  train Loss: 79.5044   val Loss: 75.2772   time: 1.18s   best: 75.2772\n",
      "Epoch 192/1000:  train Loss: 77.5826   val Loss: 76.5771   time: 1.17s   best: 75.2772\n",
      "Epoch 193/1000:  train Loss: 77.7775   val Loss: 75.5287   time: 1.18s   best: 75.2772\n",
      "Epoch 194/1000:  train Loss: 77.9657   val Loss: 74.9763   time: 1.21s   best: 74.9763\n",
      "Epoch 195/1000:  train Loss: 77.4079   val Loss: 74.7021   time: 1.21s   best: 74.7021\n",
      "Epoch 196/1000:  train Loss: 76.6128   val Loss: 76.5565   time: 1.21s   best: 74.7021\n",
      "Epoch 197/1000:  train Loss: 77.0512   val Loss: 74.7528   time: 1.21s   best: 74.7021\n",
      "Epoch 198/1000:  train Loss: 76.5890   val Loss: 75.0714   time: 1.21s   best: 74.7021\n",
      "Epoch 199/1000:  train Loss: 76.3896   val Loss: 74.0007   time: 1.21s   best: 74.0007\n",
      "Epoch 200/1000:  train Loss: 76.1349   val Loss: 74.1557   time: 1.20s   best: 74.0007\n",
      "Epoch 201/1000:  train Loss: 76.1122   val Loss: 74.3509   time: 1.19s   best: 74.0007\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/1000:  train Loss: 75.8500   val Loss: 74.3534   time: 1.18s   best: 74.0007\n",
      "Epoch 203/1000:  train Loss: 76.1449   val Loss: 74.3232   time: 1.18s   best: 74.0007\n",
      "Epoch 204/1000:  train Loss: 76.3877   val Loss: 73.7143   time: 1.18s   best: 73.7143\n",
      "Epoch 205/1000:  train Loss: 75.5532   val Loss: 74.1380   time: 1.18s   best: 73.7143\n",
      "Epoch 206/1000:  train Loss: 75.5515   val Loss: 73.5351   time: 1.18s   best: 73.5351\n",
      "Epoch 207/1000:  train Loss: 75.6099   val Loss: 73.9841   time: 1.18s   best: 73.5351\n",
      "Epoch 208/1000:  train Loss: 76.2276   val Loss: 73.6676   time: 1.17s   best: 73.5351\n",
      "Epoch 209/1000:  train Loss: 75.1598   val Loss: 72.9734   time: 1.17s   best: 72.9734\n",
      "Epoch 210/1000:  train Loss: 75.1452   val Loss: 73.2564   time: 1.19s   best: 72.9734\n",
      "Epoch 211/1000:  train Loss: 75.1764   val Loss: 73.9087   time: 1.21s   best: 72.9734\n",
      "Epoch 212/1000:  train Loss: 75.4867   val Loss: 72.8098   time: 1.21s   best: 72.8098\n",
      "Epoch 213/1000:  train Loss: 74.8537   val Loss: 72.2113   time: 1.21s   best: 72.2113\n",
      "Epoch 214/1000:  train Loss: 74.6208   val Loss: 72.7904   time: 1.21s   best: 72.2113\n",
      "Epoch 215/1000:  train Loss: 74.0517   val Loss: 72.9769   time: 1.21s   best: 72.2113\n",
      "Epoch 216/1000:  train Loss: 74.3678   val Loss: 72.3476   time: 1.21s   best: 72.2113\n",
      "Epoch 217/1000:  train Loss: 74.3434   val Loss: 73.7995   time: 1.19s   best: 72.2113\n",
      "Epoch 218/1000:  train Loss: 74.2723   val Loss: 72.6475   time: 1.18s   best: 72.2113\n",
      "Epoch 219/1000:  train Loss: 74.1695   val Loss: 72.7566   time: 1.18s   best: 72.2113\n",
      "Epoch 220/1000:  train Loss: 74.0261   val Loss: 73.1734   time: 1.19s   best: 72.2113\n",
      "Epoch 221/1000:  train Loss: 73.7335   val Loss: 71.6713   time: 1.18s   best: 71.6713\n",
      "Epoch 222/1000:  train Loss: 74.1116   val Loss: 72.9390   time: 1.18s   best: 71.6713\n",
      "Epoch 223/1000:  train Loss: 74.1896   val Loss: 72.7505   time: 1.18s   best: 71.6713\n",
      "Epoch 224/1000:  train Loss: 73.9422   val Loss: 72.8598   time: 1.18s   best: 71.6713\n",
      "Epoch 225/1000:  train Loss: 73.6003   val Loss: 71.6395   time: 1.17s   best: 71.6395\n",
      "Epoch 226/1000:  train Loss: 73.3089   val Loss: 72.1030   time: 1.19s   best: 71.6395\n",
      "Epoch 227/1000:  train Loss: 73.5709   val Loss: 71.5611   time: 1.21s   best: 71.5611\n",
      "Epoch 228/1000:  train Loss: 73.5889   val Loss: 72.8729   time: 1.21s   best: 71.5611\n",
      "Epoch 229/1000:  train Loss: 74.6347   val Loss: 73.5145   time: 1.22s   best: 71.5611\n",
      "Epoch 230/1000:  train Loss: 73.8777   val Loss: 72.2662   time: 1.24s   best: 71.5611\n",
      "Epoch 231/1000:  train Loss: 73.3483   val Loss: 71.5615   time: 1.21s   best: 71.5611\n",
      "Epoch 232/1000:  train Loss: 72.9251   val Loss: 71.3831   time: 1.21s   best: 71.3831\n",
      "Epoch 233/1000:  train Loss: 72.9240   val Loss: 71.4548   time: 1.20s   best: 71.3831\n",
      "Epoch 234/1000:  train Loss: 72.6489   val Loss: 72.0986   time: 1.19s   best: 71.3831\n",
      "Epoch 235/1000:  train Loss: 72.8854   val Loss: 71.3256   time: 1.19s   best: 71.3256\n",
      "Epoch 236/1000:  train Loss: 72.3880   val Loss: 70.8592   time: 1.22s   best: 70.8592\n",
      "Epoch 237/1000:  train Loss: 72.3969   val Loss: 70.9124   time: 1.19s   best: 70.8592\n",
      "Epoch 238/1000:  train Loss: 72.4088   val Loss: 70.8264   time: 1.18s   best: 70.8264\n",
      "Epoch 239/1000:  train Loss: 72.4916   val Loss: 70.5415   time: 1.18s   best: 70.5415\n",
      "Epoch 240/1000:  train Loss: 72.1609   val Loss: 71.1328   time: 1.17s   best: 70.5415\n",
      "Epoch 241/1000:  train Loss: 72.0773   val Loss: 71.1583   time: 1.17s   best: 70.5415\n",
      "Epoch 242/1000:  train Loss: 71.9605   val Loss: 71.5724   time: 1.18s   best: 70.5415\n",
      "Epoch 243/1000:  train Loss: 73.4069   val Loss: 70.8813   time: 1.20s   best: 70.5415\n",
      "Epoch 244/1000:  train Loss: 72.7421   val Loss: 71.9059   time: 1.20s   best: 70.5415\n",
      "Epoch 245/1000:  train Loss: 72.0276   val Loss: 70.8986   time: 1.21s   best: 70.5415\n",
      "Epoch 246/1000:  train Loss: 72.0465   val Loss: 71.0635   time: 1.21s   best: 70.5415\n",
      "Epoch 247/1000:  train Loss: 71.9126   val Loss: 71.3163   time: 1.21s   best: 70.5415\n",
      "Epoch 248/1000:  train Loss: 72.2106   val Loss: 71.2389   time: 1.21s   best: 70.5415\n",
      "Epoch 249/1000:  train Loss: 71.7816   val Loss: 70.4057   time: 1.20s   best: 70.4057\n",
      "Epoch 250/1000:  train Loss: 71.4411   val Loss: 72.4584   time: 1.19s   best: 70.4057\n",
      "Epoch 251/1000:  train Loss: 71.5498   val Loss: 70.1591   time: 1.18s   best: 70.1591\n",
      "Epoch 252/1000:  train Loss: 71.6382   val Loss: 69.9567   time: 1.18s   best: 69.9567\n",
      "Epoch 253/1000:  train Loss: 71.0879   val Loss: 69.9706   time: 1.18s   best: 69.9567\n",
      "Epoch 254/1000:  train Loss: 71.0064   val Loss: 70.4410   time: 1.18s   best: 69.9567\n",
      "Epoch 255/1000:  train Loss: 70.7104   val Loss: 72.3189   time: 1.18s   best: 69.9567\n",
      "Epoch 256/1000:  train Loss: 71.6879   val Loss: 69.9461   time: 1.18s   best: 69.9461\n",
      "Epoch 257/1000:  train Loss: 70.8765   val Loss: 70.7203   time: 1.17s   best: 69.9461\n",
      "Epoch 258/1000:  train Loss: 71.0224   val Loss: 69.8103   time: 1.17s   best: 69.8103\n",
      "Epoch 259/1000:  train Loss: 70.7559   val Loss: 70.2163   time: 1.19s   best: 69.8103\n",
      "Epoch 260/1000:  train Loss: 70.9904   val Loss: 69.4008   time: 1.21s   best: 69.4008\n",
      "Epoch 261/1000:  train Loss: 71.9931   val Loss: 70.5331   time: 1.21s   best: 69.4008\n",
      "Epoch 262/1000:  train Loss: 70.7317   val Loss: 70.5585   time: 1.21s   best: 69.4008\n",
      "Epoch 263/1000:  train Loss: 70.7280   val Loss: 69.4643   time: 1.21s   best: 69.4008\n",
      "Epoch 264/1000:  train Loss: 70.4316   val Loss: 71.5580   time: 1.21s   best: 69.4008\n",
      "Epoch 265/1000:  train Loss: 70.7135   val Loss: 69.9668   time: 1.21s   best: 69.4008\n",
      "Epoch 266/1000:  train Loss: 70.3980   val Loss: 68.7957   time: 1.19s   best: 68.7957\n",
      "Epoch 267/1000:  train Loss: 70.0139   val Loss: 69.8403   time: 1.19s   best: 68.7957\n",
      "Epoch 268/1000:  train Loss: 70.3677   val Loss: 69.2724   time: 1.18s   best: 68.7957\n",
      "Epoch 269/1000:  train Loss: 70.0315   val Loss: 69.0139   time: 1.18s   best: 68.7957\n",
      "Epoch 270/1000:  train Loss: 70.0992   val Loss: 70.5251   time: 1.18s   best: 68.7957\n",
      "Epoch 271/1000:  train Loss: 70.1836   val Loss: 68.9083   time: 1.18s   best: 68.7957\n",
      "Epoch 272/1000:  train Loss: 69.8247   val Loss: 68.4822   time: 1.18s   best: 68.4822\n",
      "Epoch 273/1000:  train Loss: 69.6837   val Loss: 68.5359   time: 1.17s   best: 68.4822\n",
      "Epoch 274/1000:  train Loss: 69.6399   val Loss: 69.2744   time: 1.17s   best: 68.4822\n",
      "Epoch 275/1000:  train Loss: 70.1465   val Loss: 71.2159   time: 1.19s   best: 68.4822\n",
      "Epoch 276/1000:  train Loss: 71.4323   val Loss: 69.1060   time: 1.21s   best: 68.4822\n",
      "Epoch 277/1000:  train Loss: 69.6339   val Loss: 68.8448   time: 1.20s   best: 68.4822\n",
      "Epoch 278/1000:  train Loss: 69.3835   val Loss: 68.8494   time: 1.21s   best: 68.4822\n",
      "Epoch 279/1000:  train Loss: 69.4651   val Loss: 69.1578   time: 1.21s   best: 68.4822\n",
      "Epoch 280/1000:  train Loss: 70.7522   val Loss: 69.7548   time: 1.21s   best: 68.4822\n",
      "Epoch 281/1000:  train Loss: 69.1021   val Loss: 68.3110   time: 1.21s   best: 68.3110\n",
      "Epoch 282/1000:  train Loss: 69.0982   val Loss: 68.5077   time: 1.20s   best: 68.3110\n",
      "Epoch 283/1000:  train Loss: 69.7378   val Loss: 68.6566   time: 1.18s   best: 68.3110\n",
      "Epoch 284/1000:  train Loss: 69.2460   val Loss: 69.1520   time: 1.18s   best: 68.3110\n",
      "Epoch 285/1000:  train Loss: 69.1009   val Loss: 68.2058   time: 1.18s   best: 68.2058\n",
      "Epoch 286/1000:  train Loss: 68.9179   val Loss: 67.8858   time: 1.18s   best: 67.8858\n",
      "Epoch 287/1000:  train Loss: 69.0515   val Loss: 69.6135   time: 1.19s   best: 67.8858\n",
      "Epoch 288/1000:  train Loss: 69.0998   val Loss: 68.1641   time: 1.18s   best: 67.8858\n",
      "Epoch 289/1000:  train Loss: 68.8106   val Loss: 67.7322   time: 1.18s   best: 67.7322\n",
      "Epoch 290/1000:  train Loss: 69.2586   val Loss: 69.0200   time: 1.17s   best: 67.7322\n",
      "Epoch 291/1000:  train Loss: 68.9220   val Loss: 69.6587   time: 1.19s   best: 67.7322\n",
      "Epoch 292/1000:  train Loss: 69.2114   val Loss: 69.3497   time: 1.21s   best: 67.7322\n",
      "Epoch 293/1000:  train Loss: 68.9103   val Loss: 68.6221   time: 1.21s   best: 67.7322\n",
      "Epoch 294/1000:  train Loss: 68.5950   val Loss: 68.0798   time: 1.21s   best: 67.7322\n",
      "Epoch 295/1000:  train Loss: 68.2261   val Loss: 67.5732   time: 1.21s   best: 67.5732\n",
      "Epoch 296/1000:  train Loss: 68.4291   val Loss: 68.2964   time: 1.20s   best: 67.5732\n",
      "Epoch 297/1000:  train Loss: 68.6403   val Loss: 67.5911   time: 1.21s   best: 67.5732\n",
      "Epoch 298/1000:  train Loss: 69.1147   val Loss: 69.3946   time: 1.20s   best: 67.5732\n",
      "Epoch 299/1000:  train Loss: 68.5359   val Loss: 67.7714   time: 1.19s   best: 67.5732\n",
      "Epoch 300/1000:  train Loss: 67.9349   val Loss: 68.3667   time: 1.19s   best: 67.5732\n",
      "Epoch 301/1000:  train Loss: 68.2438   val Loss: 68.1080   time: 1.18s   best: 67.5732\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/1000:  train Loss: 67.9873   val Loss: 68.3752   time: 1.18s   best: 67.5732\n",
      "Epoch 303/1000:  train Loss: 68.4329   val Loss: 67.6274   time: 1.18s   best: 67.5732\n",
      "Epoch 304/1000:  train Loss: 68.0699   val Loss: 70.5335   time: 1.18s   best: 67.5732\n",
      "Epoch 305/1000:  train Loss: 68.4669   val Loss: 67.2174   time: 1.17s   best: 67.2174\n",
      "Epoch 306/1000:  train Loss: 67.7441   val Loss: 67.7842   time: 1.17s   best: 67.2174\n",
      "Epoch 307/1000:  train Loss: 67.7313   val Loss: 66.8958   time: 1.17s   best: 66.8958\n",
      "Epoch 308/1000:  train Loss: 67.5204   val Loss: 67.4694   time: 1.19s   best: 66.8958\n",
      "Epoch 309/1000:  train Loss: 67.6411   val Loss: 66.7231   time: 1.20s   best: 66.7231\n",
      "Epoch 310/1000:  train Loss: 67.4067   val Loss: 67.2291   time: 1.21s   best: 66.7231\n",
      "Epoch 311/1000:  train Loss: 67.4128   val Loss: 67.0527   time: 1.21s   best: 66.7231\n",
      "Epoch 312/1000:  train Loss: 67.3368   val Loss: 66.6348   time: 1.25s   best: 66.6348\n",
      "Epoch 313/1000:  train Loss: 66.9104   val Loss: 66.6474   time: 1.31s   best: 66.6348\n",
      "Epoch 314/1000:  train Loss: 67.0607   val Loss: 67.4786   time: 1.30s   best: 66.6348\n",
      "Epoch 315/1000:  train Loss: 67.0666   val Loss: 67.3254   time: 1.29s   best: 66.6348\n",
      "Epoch 316/1000:  train Loss: 67.5717   val Loss: 66.2686   time: 1.27s   best: 66.2686\n",
      "Epoch 317/1000:  train Loss: 67.1281   val Loss: 66.5157   time: 1.24s   best: 66.2686\n",
      "Epoch 318/1000:  train Loss: 66.9959   val Loss: 67.1466   time: 1.24s   best: 66.2686\n",
      "Epoch 319/1000:  train Loss: 67.0587   val Loss: 66.3318   time: 1.19s   best: 66.2686\n",
      "Epoch 320/1000:  train Loss: 66.8811   val Loss: 66.1947   time: 1.18s   best: 66.1947\n",
      "Epoch 321/1000:  train Loss: 66.6718   val Loss: 66.6438   time: 1.17s   best: 66.1947\n",
      "Epoch 322/1000:  train Loss: 66.9657   val Loss: 66.1163   time: 1.17s   best: 66.1163\n",
      "Epoch 323/1000:  train Loss: 66.7473   val Loss: 68.1458   time: 1.17s   best: 66.1163\n",
      "Epoch 324/1000:  train Loss: 66.7447   val Loss: 66.4006   time: 1.18s   best: 66.1163\n",
      "Epoch 325/1000:  train Loss: 66.4042   val Loss: 66.3386   time: 1.21s   best: 66.1163\n",
      "Epoch 326/1000:  train Loss: 66.6461   val Loss: 66.3034   time: 1.21s   best: 66.1163\n",
      "Epoch 327/1000:  train Loss: 66.3662   val Loss: 65.4189   time: 1.22s   best: 65.4189\n",
      "Epoch 328/1000:  train Loss: 66.2953   val Loss: 66.1650   time: 1.20s   best: 65.4189\n",
      "Epoch 329/1000:  train Loss: 66.3881   val Loss: 68.1350   time: 1.21s   best: 65.4189\n",
      "Epoch 330/1000:  train Loss: 66.6838   val Loss: 67.1714   time: 1.21s   best: 65.4189\n",
      "Epoch 331/1000:  train Loss: 66.3989   val Loss: 68.1102   time: 1.20s   best: 65.4189\n",
      "Epoch 332/1000:  train Loss: 66.7421   val Loss: 66.4321   time: 1.19s   best: 65.4189\n",
      "Epoch 333/1000:  train Loss: 66.2657   val Loss: 66.9839   time: 1.18s   best: 65.4189\n",
      "Epoch 334/1000:  train Loss: 65.9975   val Loss: 66.8172   time: 1.18s   best: 65.4189\n",
      "Epoch 335/1000:  train Loss: 65.9878   val Loss: 65.5703   time: 1.18s   best: 65.4189\n",
      "Epoch 336/1000:  train Loss: 65.7758   val Loss: 65.4848   time: 1.18s   best: 65.4189\n",
      "Epoch 337/1000:  train Loss: 65.5084   val Loss: 65.6162   time: 1.19s   best: 65.4189\n",
      "Epoch 338/1000:  train Loss: 65.7842   val Loss: 66.4794   time: 1.18s   best: 65.4189\n",
      "Epoch 339/1000:  train Loss: 65.8216   val Loss: 64.8716   time: 1.17s   best: 64.8716\n",
      "Epoch 340/1000:  train Loss: 65.4057   val Loss: 64.4474   time: 1.17s   best: 64.4474\n",
      "Epoch 341/1000:  train Loss: 65.9902   val Loss: 66.2684   time: 1.18s   best: 64.4474\n",
      "Epoch 342/1000:  train Loss: 66.4208   val Loss: 65.3331   time: 1.21s   best: 64.4474\n",
      "Epoch 343/1000:  train Loss: 66.0047   val Loss: 65.2632   time: 1.22s   best: 64.4474\n",
      "Epoch 344/1000:  train Loss: 65.2605   val Loss: 64.9330   time: 1.21s   best: 64.4474\n",
      "Epoch 345/1000:  train Loss: 65.1970   val Loss: 66.1653   time: 1.22s   best: 64.4474\n",
      "Epoch 346/1000:  train Loss: 65.7128   val Loss: 65.1018   time: 1.21s   best: 64.4474\n",
      "Epoch 347/1000:  train Loss: 65.4058   val Loss: 65.8376   time: 1.20s   best: 64.4474\n",
      "Epoch 348/1000:  train Loss: 64.8806   val Loss: 65.1893   time: 1.19s   best: 64.4474\n",
      "Epoch 349/1000:  train Loss: 64.7778   val Loss: 64.7055   time: 1.18s   best: 64.4474\n",
      "Epoch 350/1000:  train Loss: 65.1467   val Loss: 65.3457   time: 1.18s   best: 64.4474\n",
      "Epoch 351/1000:  train Loss: 65.2859   val Loss: 65.3712   time: 1.18s   best: 64.4474\n",
      "Epoch 352/1000:  train Loss: 65.1974   val Loss: 64.6869   time: 1.18s   best: 64.4474\n",
      "Epoch 353/1000:  train Loss: 65.4692   val Loss: 64.6093   time: 1.18s   best: 64.4474\n",
      "Epoch 354/1000:  train Loss: 65.4359   val Loss: 65.5655   time: 1.16s   best: 64.4474\n",
      "Epoch 355/1000:  train Loss: 64.6376   val Loss: 65.5153   time: 1.17s   best: 64.4474\n",
      "Epoch 356/1000:  train Loss: 65.2248   val Loss: 63.9584   time: 1.17s   best: 63.9584\n",
      "Epoch 357/1000:  train Loss: 64.3041   val Loss: 64.8470   time: 1.18s   best: 63.9584\n",
      "Epoch 358/1000:  train Loss: 64.9930   val Loss: 64.2670   time: 1.21s   best: 63.9584\n",
      "Epoch 359/1000:  train Loss: 64.8888   val Loss: 65.1135   time: 1.21s   best: 63.9584\n",
      "Epoch 360/1000:  train Loss: 64.6299   val Loss: 64.7829   time: 1.21s   best: 63.9584\n",
      "Epoch 361/1000:  train Loss: 64.6201   val Loss: 65.8016   time: 1.20s   best: 63.9584\n",
      "Epoch 362/1000:  train Loss: 65.2593   val Loss: 64.2027   time: 1.21s   best: 63.9584\n",
      "Epoch 363/1000:  train Loss: 64.5422   val Loss: 64.2795   time: 1.21s   best: 63.9584\n",
      "Epoch 364/1000:  train Loss: 64.4524   val Loss: 65.2539   time: 1.19s   best: 63.9584\n",
      "Epoch 365/1000:  train Loss: 64.1725   val Loss: 64.0852   time: 1.18s   best: 63.9584\n",
      "Epoch 366/1000:  train Loss: 64.6977   val Loss: 63.8190   time: 1.18s   best: 63.8190\n",
      "Epoch 367/1000:  train Loss: 64.0968   val Loss: 64.8608   time: 1.17s   best: 63.8190\n",
      "Epoch 368/1000:  train Loss: 64.2043   val Loss: 63.2992   time: 1.18s   best: 63.2992\n",
      "Epoch 369/1000:  train Loss: 63.7428   val Loss: 63.7170   time: 1.17s   best: 63.2992\n",
      "Epoch 370/1000:  train Loss: 64.9529   val Loss: 65.0387   time: 1.18s   best: 63.2992\n",
      "Epoch 371/1000:  train Loss: 64.3353   val Loss: 63.2749   time: 1.17s   best: 63.2749\n",
      "Epoch 372/1000:  train Loss: 63.6907   val Loss: 63.8047   time: 1.17s   best: 63.2749\n",
      "Epoch 373/1000:  train Loss: 64.2808   val Loss: 63.4377   time: 1.17s   best: 63.2749\n",
      "Epoch 374/1000:  train Loss: 63.6448   val Loss: 63.9245   time: 1.20s   best: 63.2749\n",
      "Epoch 375/1000:  train Loss: 63.5843   val Loss: 63.4764   time: 1.22s   best: 63.2749\n",
      "Epoch 376/1000:  train Loss: 63.7702   val Loss: 65.0774   time: 1.21s   best: 63.2749\n",
      "Epoch 377/1000:  train Loss: 63.8211   val Loss: 63.2820   time: 1.22s   best: 63.2749\n",
      "Epoch 378/1000:  train Loss: 63.6219   val Loss: 64.8902   time: 1.22s   best: 63.2749\n",
      "Epoch 379/1000:  train Loss: 63.8635   val Loss: 63.4936   time: 1.21s   best: 63.2749\n",
      "Epoch 380/1000:  train Loss: 63.3198   val Loss: 62.8060   time: 1.20s   best: 62.8060\n",
      "Epoch 381/1000:  train Loss: 63.5185   val Loss: 63.8162   time: 1.19s   best: 62.8060\n",
      "Epoch 382/1000:  train Loss: 63.5901   val Loss: 63.0020   time: 1.19s   best: 62.8060\n",
      "Epoch 383/1000:  train Loss: 63.4655   val Loss: 63.9592   time: 1.19s   best: 62.8060\n",
      "Epoch 384/1000:  train Loss: 63.3577   val Loss: 63.2627   time: 1.19s   best: 62.8060\n",
      "Epoch 385/1000:  train Loss: 63.2123   val Loss: 64.4935   time: 1.18s   best: 62.8060\n",
      "Epoch 386/1000:  train Loss: 63.2916   val Loss: 62.8044   time: 1.18s   best: 62.8044\n",
      "Epoch 387/1000:  train Loss: 62.9219   val Loss: 63.2537   time: 1.18s   best: 62.8044\n",
      "Epoch 388/1000:  train Loss: 63.1696   val Loss: 63.3968   time: 1.17s   best: 62.8044\n",
      "Epoch 389/1000:  train Loss: 62.7263   val Loss: 62.6833   time: 1.18s   best: 62.6833\n",
      "Epoch 390/1000:  train Loss: 62.8818   val Loss: 62.4788   time: 1.20s   best: 62.4788\n",
      "Epoch 391/1000:  train Loss: 62.8038   val Loss: 62.4820   time: 1.22s   best: 62.4788\n",
      "Epoch 392/1000:  train Loss: 62.9146   val Loss: 64.4787   time: 1.21s   best: 62.4788\n",
      "Epoch 393/1000:  train Loss: 63.0029   val Loss: 62.4727   time: 1.22s   best: 62.4727\n",
      "Epoch 394/1000:  train Loss: 62.8730   val Loss: 63.4543   time: 1.29s   best: 62.4727\n",
      "Epoch 395/1000:  train Loss: 62.7174   val Loss: 63.9089   time: 1.33s   best: 62.4727\n",
      "Epoch 396/1000:  train Loss: 62.8338   val Loss: 62.9534   time: 1.32s   best: 62.4727\n",
      "Epoch 397/1000:  train Loss: 62.6652   val Loss: 63.1720   time: 1.31s   best: 62.4727\n",
      "Epoch 398/1000:  train Loss: 62.3826   val Loss: 63.6595   time: 1.29s   best: 62.4727\n",
      "Epoch 399/1000:  train Loss: 62.3861   val Loss: 62.3771   time: 1.37s   best: 62.3771\n",
      "Epoch 400/1000:  train Loss: 62.2803   val Loss: 64.4461   time: 1.31s   best: 62.3771\n",
      "Epoch 401/1000:  train Loss: 62.6796   val Loss: 62.4667   time: 1.31s   best: 62.3771\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z9yMiIkfn0iHRqfkm8wMPPMD111/PkCFDOPvss8t8/dHR0QwZMoRevXqRmJhIx44dD/veiRMnUrNmzf3Pv/jiC5555hl69OiBtZbevXvTp08fFi5cyA033IDP5wPgmWeeoaSkhGuuuYbdu3djreXee++lQoUKZd6PiIiUgrU26Lf27dvbP1q6dOmflpVWbkGxXbhhp921t+C41xEqMjMzrbXW+nw+O3jwYDtkyJAgV3R0h9t2kyZNOrWFnETqJfS4pQ9r1UsoOhl9AHPsITLRnbuyXTT11wcffECbNm1o3rw5u3fv5pZbbgl2SSIichK5dFe2nxtmyr7jjjt4+OGHg12GiIicIq4cMYuIiDiVgllERCSEuDKYT9XXpURERMqaK4N5PyWziIg4jDuDuQzOyk5NTWX8+PG/W/bSSy9x2223HfEzc+bMAaB3796HnHP6iSee4Pnnnz/izx49ejRLly7d//wf//gHEyZMOIbqD02XhxQRCX2uDOay2JU9YMAAhg8f/rtlw4cPL/V81d9+++1xT9Lxx2B+8sknOffcc49rXSIi4iyuDGZP4IvMvhOYa/qKK65g3LhxFBQUALB27VoyMzPp2rUrgwcPJiUlhebNm/P4448f8vPJycls374dgKeffprGjRtz7rnn7r80JMA777xDhw4daN26NX379iU3N5fp06czduxY/u///o82bdqwevVqBg4cyJdffgn4Z/hq27YtLVu2ZNCgQfvrS05O5vHHH6ddu3a0bNmS5cuXl7pXXR5SRCR0OON7zN89BJsXl/rtXiz1CkqICPOA9zD/9qjaEi549rDrqFSpEh07duT777+nT58+DB8+nKuuugpjDE8//TQVK1akpKSEc845h0WLFtGqVatDrmfu3LkMHz6c+fPnU1xcTLt27Wjfvj0Al19+OTfddBMAf//73xk6dCh33nknl1xyCRdddBFXXHEFOTk5+9eVn5/PwIEDmThxIo0aNeK6667jzTff5J577gEgMTGRefPm8cYbb/D888/z7rvvHvV3pctDioiEFleOmA0GYw5/haXSOnh39sG7sUeMGEG7du1o27Yt6enpv9vt/EdTpkzhsssuIyYmhnLlynHJJZfsf23JkiV069aNli1bMmzYsMNeNnKfFStWULduXRo1agTA9ddfz+TJk/e/fvnllwPQvn37/Re+OBpdHlJEJLQ44y/rEUa2h7MuczfxUeHUqhhz3D/20ksv5b777mPevHnk5eXRrl071qxZw/PPP8/s2bNJSEhg4MCB5OfnH3E95jBzhA4cOJDRo0fTunVrPvjgA9LS0o64nqP9Q2PfpSMPd2nJY1mnLg8pIhIcrhwxA3gMlPhObMQcFxdHamoqgwYN2j9azs7OJjY2lvLly7Nlyxa+++67I66je/fujBo1iry8PHJycvj666/3v5aTk0O1atUoKipi2LBh+5fHx8f/bhf2PvsuA7lq1SoAPv74Y84666wT6lGXhxQRCS2uHep4OPFgBv/u7Msvv3z/Lu3WrVvTtm1bmjdvTr169ejSpcsRP9+uXTuuuuoq2rRpQ506dejWrdv+15566ik6depEnTp1aNmy5f4w7t+/PzfddBOvvPLK767zHBUVxfvvv0+/fv0oLi6mQ4cO3HrrrcfUjy4PKSIS2syJHoctCykpKXbf93/3WbZsGU2bNj3uda7avBuf8dAoKf5EywuqnJwc4uOd1cPhtl1aWhqpqamnvqCTQL2EHrf0AeolFJ2MPowxc621KX9crl3ZIiIiIcS1wexVMIuIiAOFdDCfyG52j/FPMHIik4zIsQuFQyMiIk4WssEcFRVFVlbWcf+h3zf7l0bNp461lqysLKKiooJdioiIY4XsWdk1a9YkIyODbdu2Hdfnd+/NJ6fQwq5Iwg83+5cD5OfnOyrooqKifnfWt4iIHJuQDebw8HDq1q173J9/5YsJDJlbwMjBnWlVJ6EMKzu10tLSaNu2bbDLEBGRU+SoQ0ljzHvGmK3GmCUHLXvCGLPRGLMgcOt90GsPG2NWGWNWGGN6nqzCD6u4ADYtIsH4J77Izis65SWIiIgcr9Ls4/0A6HWI5S9aa9sEbt8CGGOaAf2B5oHPvGGM8ZZVsaWyOwPe6kaDvfP8TxXMIiLiIEcNZmvtZGBHKdfXBxhurS2w1q4BVgEdT6C+YxdTCYAKdjcAm7OPPI+1iIhIKDmRY8x3GGOuA+YA91trdwI1gBkHvScjsOxPjDE3AzcDJCUlHfUCDqVmLd1NGOH5WcRHwPTFq2hiN5TNuoNgz549Zfe7CTL1Eprc0otb+gD1EopOZR/HG8xvAk8BNnD/AjAIONRllA75fSVr7dvA2+CfkrNMpzqbV4VYcmlcLYECjyE19cyyW/cp5pbp7EC9hCq39OKWPkC9hKJT2cdxfY/IWrvFWltirfUB73Bgd3UGUOugt9YEMk+sxOMQW4nwomySE2NZs33vKf/xIiIix+u4gtkYU+2gp5cB+87YHgv0N8ZEGmPqAg2BWSdW4nGIrUxE4S7qJsayNaeAvQWluzaxiIhIsB11V7Yx5jMgFUg0xmQAjwOpxpg2+HdTrwVuAbDWphtjRgBLgWLgdmttyUmp/EhiEgkvWkLdxFgA1mbtpXn18qe8DBERkWN11GC21g44xOKhR3j/08DTJ1LUCYutTERh9v5gXrNdwSwiIs7g3LkqjyQ2Ea8vn7rlDRFhHhZu2BXsikRERErFtcEMEFW4k7a1KvDL6qwgFyQiIlI6Lg3myv77vds4s34l0jOz2ZVbGNyaRERESsGdwRzjHzGzdztn1quEtTBjdWknLxMREQkedwZzXGDEnLOJtrUTiI8K48elW4Jbk4iISCm4M5jL16bYGwWblxAR5qFn86r8kL6ZguJT/80tERGRY+HOYPZ42BNXDzYtBOCiVtXIKSgmbcW2IBcmIiJyZO4MZiAnvj5sXgwlxXRpkEjl+Ei+mOPci1mIiMjpwbXBvCeuPhTnwfaVhHs9XJlSk5+Wb2XjrrxglyYiInJYrg3mnPj6/gcZswHo36E2Fvj4l3XBK0pEROQoXBvMuTG1oEJtWDoagFoVY+jdohrDZqxjd15RcIsTERE5DNcGM8ZAyythdRrk+L8qNTi1PjkFxbw7ZXVwaxMRETkM9wYzQKsrwfpg4WcAtKhRnj5tqvPWz6t1nWYREQlJ7g7myo0huRvMegdK/NdkfrR3U8K8hlcn/hrk4kRERP7M3cEMcObtkJ2xf9RcpVwUV7SvybhFm9iWUxDk4kRERH7P/cHcsCfUPhO+fxiyfgPg+s7JFJb4eCNtVZCLExER+T33B7PHA5e/A95wGNYP9mZRv3Ic151Zh/enrWXKr5oNTEREQof7gxmgQi0Y8BnszoBRN4PPxyO9m1I3MZYnv15KcYkv2BWKiIgAp0swA9Q+A3o+DasmwOx3iAr38mCvJvy6dQ9fzs0IdnUiIiLA6RTMAB1uhPrnwMQnYfdGejZPon2dBIb8uJLcwuJgVyciInKaBbMxcOEL4CuGiU9ijOGR3k3YmlPAf8evCHZ1IiIip1kwA1SsCx1vhkWfw5altK9TkYGdk3l/2lrem7om2NWJiMhp7vQLZoCu90JkPPz0FACPXdSMXs2r8tQ3S/l4xjqstUEuUERETlenZzDHVIQud8GKb2H9TLwew0v929C1QSKPjV7CE2PTg12hiIicpk7PYAboNBjiq8NXN0HuDqLCvXx4Q0du6JLMh7+s46t5OlNbREROvdM3mCPj4MqPIGcTvHM2bFqIx2N4tHdTOtWtyCOjFrM0MzvYVYqIyGnm9A1mgFod4LoxUFIE7/eGeR8RVpLHq39pS/nocK57bxartuYEu0oRETmNnN7BDFCnM9w4ARIbwtg74dk6VJn0f4xv+j1PlrzMf975mM2784NdpYiInCbCgl1ASChXDW6aBOumQfoomPMeFTxh9AyPonXBMm58vynDB3cjLlK/LhERObk0Yt7HGEju6p+A5O5FcN8yvH3foYbZRrPt33PD+7PYnVsU7CpFRMTlFMyHUqEWxCZCo55QrTX/jB/Dyg1buODlyYyan0F+UUmwKxQREZdSMB+JMdDrP0TnbeLH1mnERYVx7+cL6fPaNNZs3wtTX4RfXg92lSIi4iIK5qOpcyZ0upUqSz9gfKOvee/KemTv2s61L4ygaMK/yE97geJijaBFRKRs6Gym0uj5b/AVY+YM5exqc5mcsAvv7vV4bDHhBVnc/OoInrjhUqoXroWiPKjRLtgVi4iIQymYS8Pj9Z8UVrMDjLqFcICYStjYKphty2i16ycGvRbGV/H/JaZoF9y3zP8ZERGRY6RgPhatroKlYyCuClzwX4wtgWdrc0fJCK4p/o6Ynf7JSPJ/TSOq8TlBLlZERJxIx5iPhTHQ/1O4+GUIi4DwaDj3n9DsUiqQQ4kJY6+NZOqo/7ErtzDY1YqIiAMpmI+VMb9/fuZtcOWH0ONRvF3vYWfdi+iaN4lbXxvDko27g1OjiIg4lnZll5WzHgCg5q4N+F4dx5u59zHv7UZkX/0pnRslBbk4ERFxCo2Yy1qFWngueJbYGs04x8xhxseP8fPKbcGuSkREHELBfDKkDCLixu8paNibW71jueeTX1iXtTfYVYmIiAMomE8WY4g88xZiyKOHZz4D35/NmAUbg12ViIiEOAXzyZTcDeKSeKTmYiK8Hu4evoARczYEuyoREQlhCuaTyeOFZpeSuHkKX9/ajq4NEnngy0U89/1yikt8wa5ORERCkIL5ZGvUE4rzidgwnXevT6F/h1q8kfYbV787k63Z+cGuTkREQoyC+WSr0wXCY+DX8USFe3m2byte6NeaRRm7OeeFn3l8zBK25iigRUTET8F8soVHQb0esPhLWD8TgL7tazL2ji6c2yyJYTPXc96Qyfy4dAuZu/KCXKyIiASbgvlUOO9JiKkIH1wI8z8BoGFSPC9e1Ybx93anUmwEN300h27PTeLTmesp0vFnEZHTloL5VEhsADf9BMldYOydsGnh/pfqV45j1G1d+N817TijXkUeGbWYM5/5iTELNmKtBSC/SNd7FhE5XSiYT5XoBOj3AcQkwujbIW/X/pfKx4TTq0U1PryhI29f254aCdHcPXwBd342nzcW5NP2yR9Zs10TlIiInA4UzKdSdAL0eR22LYfXO8Hn18KaybBzLUx7mbCZr3N+syS+GtyZe85tyA9LtzB7cwlFJT7eTFu1fwQtIiLupYtYnGqNzodrRsKcobB2GiwbCxggELoxiXjbDOCecxtxS/f6jP/pZ+YVVOGjX9YxdmEm3RpW5oYuyZxZrxLmj1e6EhERx1MwB0O9s/y3onxYPMI/Ym4/EEbeCN/cDxvnQLvria7cmOolGZzVvS3NvBtIL67FT4vXMmz5GJ6rdh7dGycRGxlG/461KR8dHuyuRESkDCiYgyk8Ctpdd+B536Ew8Z/+M7dnvwsVatNx13qYey/9fcVw7Sgej5pB2IxXeS4vnFd+ag/Ah9PXMrhHA/q2q0FMhDapiIiT6RhzKKlQC/q+C/evgA43QnEBa5Kv9o+mE+rC2LsJm/02AA9EfsWvN8azqN6b9IhczmOjF9Pn35/zr3FLmbtuByU+HY8WEXEiBXMoiq4AF74A969gXfKV/sd9XgdjIK4KXPQS7FxL+CcXUy5zCv/KfpQltV7gR24j45cv6PvmL6T860c+nL4WnwJaRMRRtN8zlB18cldyF7hn0YHnFWrDnPegx6OYWW8TN/d9iCzP61V+ZGLK5Xy8OJfHx6YzdmEmV6XU4sJW1YiN1OYWEQl1+kvtVA3O8d8ALn4Jzn8Klo3DO/pWzv+2K+cld2Nn1V08t+08HhiZwn++X07NhGi6NEikcdV4zm5ShfgonTAmIhJqFMxuERkPrftDbCJsmIlZOJyKHi/P+oZwb5tr8a6awFsltzPi50pst+WomRDDC/1aUz4mnORKsUSFe4PdgYiIoGB2F2Og4Xn+29l/h+IC+OIGkpZ/DMCjBS/xSGQWWXUu4OeN8Mg7PejhWcCvlc7mstRORG9dQN3mnWhUq0qQGxEROX0pmN0sLBL6vQ/zP4awKBhzOyahLonrvqUvcEH8TGIKs5iYvYoqo5+ipWctI6d15Zl6j9O9UWV6tahKtfLRx//z92wDWwLxVcusJRERt1Mwu11YpP+rVwCVm0BSC9i6FFZPImbikwCcw0zwQG6t7vTdMJk5G77luRUteWlCPI/U/ZUmTVvRukO3Y//ZowdD4R4Y9H0ZNiQi4m4K5tNJzRT/fY12/oDO3QHV2sBXN0KzS4m55FV4qzvP7HyRf0cZpkSfS/fVP1LwWzhP/3QXexpeRosa5WhZozyNq8YTGXaU49KZ86GkCKz9/RnmIiJyWEcNZmPMe8BFwFZrbYvAsorA50AysBa40lq7M/Daw8BfgRLgLmvt+JNSuZyYsAjo+bT/cVQ5qNnBf3/7LFg9CTP3Q7qv+AYbXZFdUXV5eOcQPl60gtdm9WQP0bQI20BSnaa0a9GUmIgwzqiQTc3E8lC+hn+de7ZB7nb/473b/N+/FhGRoyrNiPkD4DXgo4OWPQRMtNY+a4x5KPD8QWNMM6A/0ByoDkwwxjSy1uqCwqGsUc8Dj8Mi/M+Tu8GomzHNLyOp0QUw9g6uSx/N1ZXm4CvKI6JwNwUbw/ltQ3W+LjmTbmHjyfZaxrR/n84Z75K4O53y+9aZterQwbw7A3K2QM32p6JLERFHOOrMX9baycCOPyzuA3wYePwhcOlBy4dbawustWuAVUDHsilVTqmIGLjqE2jR1//4ivcwt0wmzBYRUaEG9P+M8PbX0CgplgfDh5NkdhLuy6fPrKupv+kbyueu3b+qj8ZNIK/wEP82G3cvfHwZ+PTvNhGRfY73GHOStXYTgLV2kzFm33CoBjDjoPdlBJaJG1RtAXcv9J/h7Q3H06Q3nvxseLMLJNQhutezmI/6sjesKrHZqwEoIpySTUv4y2vfUyXSMj93AdUrV6RxuWJarfoJjy3GtzkdT5Um/hPFYioGuUkRkeAy1h59LmVjTDIw7qBjzLustRUOen2ntTbBGPM68Iu19pPA8qHAt9bakYdY583AzQBJSUnthw8fXgbtHLBnzx7i4uLKdJ3B4IQ+vMW5WOPF543EU1IIWM6YcTNF4eWIKNxJeHEOxXjJt+FkE8uQ4itI9SzgIu9MAN6IGMT5CVuovWMqMzu9RUmY/ytaFbPmUXnbFFY2uh3rCa3zFJ2wXUrLLb24pQ9QL6HoZPTRo0ePudbalD8uP96/dluMMdUCo+VqwNbA8gyg1kHvqwlkHmoF1tq3gbcBUlJSbGpq6nGWcmhpaWmU9TqDwbF9dF9OBMCEJ+DXHwlr1JOctSuptmsOz+e/BUB+Ult8uzbSIH8xVTanE2FymfjzD0yp0Ie/to2n88pXiCjYibdxTxK734IH6x9VR5ULZmeAg7fLIbilF7f0AeolFJ3KPo43mMcC1wPPBu7HHLT8U2PMEPwnfzUEZp1okeJA4YGJSS74j/8GrEhLo1rzarBjDTQ4lyiPF768gfPTRwGQG1aBeyK+BWrRJu0DMNmstDWomvYEP/48hnqR2dQu+o03Wwznhl5dKB/zh7m+C3JgwWfY9gPZvLfkxCZHEREJktJ8XeozIBVINMZkAI/jD+QRxpi/AuuBfgDW2nRjzAhgKVAM3K4zsuV3Kjf23/Y55x+QmwWFucSc/xQxn/XniZx/4osuz2/dh5ITWZ3oOf+lQ9ZivIV7ibR5lMwbxjlLi2kYtRuzdyvnnt2TXi2rUW3pB5gf/s63y7K4fXkrhl6fwjlNk/5Uwubd+fR7azqvDWhH61oVTl3vIiKlcNRgttYOOMxL5xzm/U8DT59IUXIaqVgPrv/6wPPB0yFzAZ7krjSMruBflvKl/95a+PBi7tg+jVZhcN6uEQBM+OFzVv1QTG5YNg2A5qvfJ9o7hP98v5wz6lVi+m9ZdG+UuH9ClPHpm9mwI4/vlmxWMItIyAmtM2pEytf03w7FGOh6D5Gf9uc83who/RdseAznzBmKNV48tpgFNKKNZyXfN/iG11bE0empbPYUe2hTqwIPnlePMxpW5afl/lMiZq3JOoWNiYiUjoJZnKXBuXDPIti0CBr1xBgD5/0Ts2g4fPcgbQYPg19eo868D/lvOHQpt5dNre8ka8pQ2g77gP/G30OX7BVsD+vGogxDbmExMRH630BEQof+IonzlKvuv+0TGee/UEfLfhBVHno/75+9bPGXXJo+DKYMA8BnPDyw57/ggUHe7/mpuCWzXv6IdtGbie/zHKZWYC6cNZOhejv/ekVETjEFs7hHVGAS0LAIaHIh1OnsD/DIeKhQB09ELIy4FrrcDfl7aLv4e8L3rsSzp4jl7w9mUa+R9K2ZTdiHF/NFueuoccnjdG6QGNyeROS0o2AW94pOOHChjn3uWwbx1QgzhsoXDyG/qIT5X7/JmYseZeLYfzDCm8dfDNTeNYvnflypYBaRU07BLKeXg3eBA1HhXs689DasSeeOhZ8C4MNLincVy9Zt4of0zZzXLMl/LFtE5BQ46kUsRFzP48Fc+ob/oh2tB+Dp+S+8tphesSu5+eO53DZsHtn5RcGuUkROExoxi4D/q1hNL/bfCnNh2ss873uXDm3f4JFpm5m4bCt92lTn2jPr8J/vl9M8ppjUYNcsIq6kEbPIH0XEwMBv8BgPAzKeYszgjvTvWIsxCzO55LVpTFuVxZhVhZTmAjAiIsdKwSxyKIkN4KIhsHkxrZYO4clLmpP2t1Ru7l6PAR1rsyXXMnPNHy9TLiJy4rQrW+Rwml4MnW6FGW/AuulUP/8pHundnb0FxYyZt56/vDODqzrUolvDyrSsUZ5aFWOCXbGIuIBGzCJH0utZ6PUfyN8Nnw2ATQuJjQzjH2dGc92ZyXw2awO3DZvHNUNnsjtPJ4iJyIlTMIsciTFwxq1ww3cQVQGG9YPl31ArPJsnLmnOd3d348WrWrNxZx5/eWcGyzZlB7tiEXE4BbNIaZSrBtd8CSWFMPwvdJl+PUx6hqbVynFZ25q8cXU7tmTnc/GrU/nfz7+xNTtfX7ESkeOiYBYprSpN4e5FMGg8Wyt3gZ+fhXfPgzF3cH61XH689yzOb57Es98tp+O/J9Lp6Yl8MmNdsKsWEYfRyV8ixyKqHNQ+g+VN7qFKxfKwdzssGQmrJpBwzUheH9CW4Q0z2JlbSNqKbTzz7TJ6Nq/Kw18tJjLMw6sD2uLxaBYxETk8BbPIcfB5I2DAZ/4nm5fA+73hzc6Ylv0Y0KwPVK3MOU1a0POlyVz86lQ2Z+cD0KBKHPee1yiIlYtIqFMwi5yoqi3g9hkw402Y/gos/gKAxmc/xo1de/PL6ixu7FaXZZtyeHnir0RHeLn1rPpBLlpEQpWCWaQslKsO5z3pvw+Lgt9+grRn+PttfeCibpD1G8VNy1FYUp1nv1uOz1puS20Q7KpFJAQpmEXKijFwxmD/4yYXwuo0+PwaaHYpTH2RsJhKvDj4FzwGnvt+BRt25NGzeRKta1Yga28hHgP1KscFswMRCQEKZpGTIa4KXPkRfHWz/+ztGimwcS5h4x/ihSteJyYijC/mbOCzWev3fyQ63MuHgzrSITlBl5kUOY0pmEVOlvo94J7FUJQLMRVh0r/h5/8QFp3AM5c/w2MXNWXuup2s2JxDuNfD0KlruPKtX2hSNZ6X+7elfuVYJv+6jV9+y+LBXk0I8+rbjSKnAwWzyMkUHuW/AaQ+7J/ac8YbUL4mMXW7061eU7o1rAxA75bV+GZRJi9O+JWeL00mMS6CnPxiCop9LMzYTZtaFXj4giYaTYu4nIJZ5FQxBs57CjLmwPhH/MvCoqHR+XDBc1SOr8rALnU5t1kSk5ZvZdyiTWzLKeC85kl8OSeDWWt2UL9yLB3rVqJuYuxhf0zmrjw++mUd953XiIgwjbJFnEbBLHIqhUX4593etgy2/wobZsK8j2DNZLj4ZajSnJqrJnBtYgOuveVcrLUYY3igZxMufGUKD45cjNdjuLpTbXbsLeTKlFp0a5j4u1H0+9PW8M6UNbSpVZ5eLaoFsVkROR4KZpFTLSwCqrX231peAR1vhlG3wIjr/CPo4jz/+1oPwLTsB1i89c/hhcsa8c2yXcxYncVHv6wjPiqMcYs20bBKHB3qVqRX86p0bZDIN4s2AfDl3AyqV4gmY2ce5zVLIlzHqEUcQcEsEmyJDf2j6HH3QtYq6PMGLPocpg6BhYHZxSo3ofnebTQfPJ387jXIXLuCGo3aMnr+RsYuzOTrBZl8OnM9TarGk7k7nzqVYpiwbCsTlm0FoGNyRd65PoUIr4foCG8QmxWRo1Ewi4SCsEi49I0Dz89+FFr3h20rYM57sG4a+EpgzB1E5e+i3sa5MGg8V3XoyFUdalNQXMKI2RsYMSeDtrUr8MJljfll4hjimp9PQQn8Y9QCnnjhRUbvbcY1nZIZ2CWZ+n/4zvSOvYVUjI04xY2LyB8pmEVCVaX6/lujXlCYA/M+hh8eBeOB6IrwxQ3Q+AIoyiMyZRDXtq7LtWd29X/2+0eot+p1aPwCdLiRRhu+oM3Cp6lf85+8MNPw8Yx11KscS7XyUST4ChmyZCqLMnbz8AVNuOWs+sxbv5PHRi/hgV5NOKtR5eD+HkROMwpmkVDn8UBUeeh8B7ToC3k7/d+NHncvzP8YPGGw4BMwXqh3FlgfrJsO3gj44R9QsT5tto8D4I7yU7jygh58u6kcU1dtJ2NnHtM2F9Gkqo8z6lXkme+W89/xKyj2WQD+74uF3H9+I7LzihmfvpkBHWvTt33NYP42RFxPwSziJOWq+W8At04Ba/1BvWoCZM6HVRP935suVx36DoUxd8DHlwY+WxNWTaDKqgkM7PMGA6/uB9tXMOWXX+navSkF8bUZOm0tuYXFxEeF07RaOW79eC4PjlwMQIWYcO7/YiFfL8rk3nMbER8VRn6Rjwox4VSJjyzVBCij52+kXHQYZzdJOkm/IBHnUzCLOJkx/lnFWl3pv/3RjRNg1luQMRfO+QdMexm2psM398O3f4OiXLoBLISoJhdx+8610OZq2LUO8lOY+fDF7MnNJ37xB0S2upxX5+YxfPYGLn9zOiU+SzuzklW2Ovlh5WhYJY6uDRNZsH4XSzdlUy8xlis71KJvu5oYAzNX7+DeEQvwGMPL/dtwUavqp/iXJeIMCmYRN4uMg273H3h+2ZuQ9Rt8cx8kNoZaHVmyfBUt4nbBzDchqgKMfxgwwP8o1/QSyuXthLVTYM147h/4DTd0rsPbYydRv7zhitn/ZHOlTnyQ/BwVfhvD55NrEBGfyBUtqzFrQy6PjlrMCz+spLDYR3TBNsbEvEpmZH1u/dQye80Orj2zDtUrRBMZ5sVjOOysZiNmb2D6b9t5+rKWxEbqz5a4m/4LFzndVKoP143Z/3R7VhqkpsIZt0JcVf/1pBuc4/+q1sQnISIOWvbzL//6TiqunsxDu9f7L2+JpVrWDB7Ouwxys7gpuT3e7PWY9F1Y62NL6wE8Zf9KbBg8tOExKuasoFXecv7eug9Pz1jHh7+sIybCi9djSIiJ4Obu9fB6DLvziqgYG8ElratTWOLjX98sJTu/mI278nj/ho7ERYZR4rOU+CwRuZth0yIg6kCPJUWwcx0k6tKa4jwKZhHxS0j237e71n/f7X5ocC5UrOcP58h4/1e3KtTxz/s95QXocg/k7YDCXPCGE7bwM8BAx5sx21dSdcUwXq8yHzxeyFkBfV6HiU9x47ZnSR00jGU7vdSfeh/F1sPdxXfw99FLflfSI18tJina4snP4fl2RaxaNI5uz+ZQuVwUewtKyNpbwMikD2m+/Tu2tnyXFZtzaFw1Hqa+iP35Pyy+YgqtmjXbv77FGbv53+Tf+FefFiTERsDeLIitdEp+vSKlpWAWkcOr1vrA4wuHQNNL/MtiKvpnLIuq4D9rHKBgj//ks0bnQ+/n/BfseK0D7N3mD+8a7f3HrxMbwceX0WDYmTTwhPvPIrclTGwQxfaL78QTHk1kUkPWL0yjYMnX1N/+E5GxRUT9ZiAsm/YVPcTkb6Wabx3rKzaj9rY0MDBv3gwemB3DtZ1q8vCvHxDjK+bTYUOZmHoLDZPiaFI1njs+m8e6rFyqxEfyeKMNMHwADPoBanU48u+hKI+ccY9y7bJO3N/v7P0XHhE5GRTMIlI6xvgvZblPTMXfvx4ZB3fMhojABTaiysNtM/y7vLMzIbqCfx21OsLNP8PS0VCQA416wpZ0POMfocqqH/yfDY+leVGuf101WkF2hn90W60N5236BCLLQbVW1Fs3CgyUmDBuqjAfT4OBbJozipjwjfis4dnwd1k55TtuLbqXYryEGcNnFUaSOcsybW4OXfAx6f3HmN3xJS5vV4NFGbvpVjOM+OnPsqXdvdSuVdt/3HvZ18QvHEqv4q0MnVqXbg0rszU7n0tfn8YjFzY9eSeyLRkJxYXQZsDJWb+EJAWziJSd6Aq/f74vvP94rDexAXT/24HndTr7d5tvWw4lhfDrD/7vZ/d8BiJi/F8Jy9vlX1/Wb5DUwj/n+KRn4Lef8NZoR/2Z/+Pfa67CRmwmP6YmmXEtqLflexp5NjIx6kGMLaEovhZh+Tuw3nw8lJBLFGf5ZvLW5HFUnzqd90ouYIN3OneHfcW3c7bzbdKt9GhShUsWv0sD4OKwmTy3citLNu7m02kruGDPSIZPPI9ezfvwyYx1dKxbiWbVy/l7ypgLFese+B2smuj//dRoX/rf58Sn/MfLFcynFQWziISGinX9N4BmfX7/WnSC/wZQo92B5T0e9t8Kc1m1vYAGsXmYctWI6nI39fKzYX4rqN4GM/01KM4nPHMeXPIaJmsVTHuJmCvegLF38Zn5N8aW0D96Jj6fhRIYFPUzPxRfww+TFnN3+Cy2R9agRuFG/hr+I6++PpvbwsbSOnw143b8xiWvJbJ10wbuDh/F17Uvonqdxvzll96kl+vO3I4vEj/nNa7Y+S4A8xMvosrVb1Mjwb9nobDYx9bXe1IcV4Pkv35woLfsTbBzjf9xzmaIr3oSfukSihTMIuJ8ETFk1LqUBqmpB5ZFJ/jnHAdociHkZ8Paqf4pTq0PGp4HyV0hexPmh0eh298I277S/57Ux4n87gFG2fuw5fZiw5JIvO4r+GIgj277ACIgP7ISxdXPpveaSVTe/RhtY1cQUZJL1sZZfLsuBW9YCS12p/HuN+/yUsRQvvGdyXZvFa7fPoZxQ/ryerPHaVSyknXrVvN40SyKd3gY997TtKhejvD1q1m9bCP1Aq18O/4bLug76LBfJyuVkiLI3njgJL99fD7Yvd6/PG/ngX8ASdAomEXk9BBVDpr0Djzx+EMZ4MzboV4qJDX3HwPfp3ITmP4qJjwac/ZjULkRDJ4OmxdCcSFR1dv4T3j7Xxc6ReZCnaugfg8qjbqVa5mAr3p7zPYVvMJr+KISOPu2TwmLKc/uSS/Qe9oztFt+NVXZgcdYfJ5wPL4SLlr/HKyH5IPKLrGGsxc/yOfr08ms34/qsYaiuGq0rVWB6AgvNROiiSzKgaxVrA+vx6qdRfRoXOXPIT7tZUh7Fu6aBxVqH1i+4BMYeyc0vhB+He+flKZ62zL/9UvpKZhF5PRmDFRt8efl9c7y3w7m8fw+tMIi4f7lv39P1ZYw/xM8zS/zT5k68Z94Wl5JdDn/seby5z0ADTtTdeSN2ErdIG8HntpnQHxVVmTuYG3lc8lcv4Y+xePwlK9J3JrviMrbTv/soeTM+xQLTPa1IsJk8KOvLeMiLuCtyJepkbcSY6twT8G/uDlxCZ29SxlZ4/9IaViT8hHQdsbHJPiK2DHlXRIu+idFJZaIMA8s+NRf94pvAMj/8Wmi6nTw/04a9/79P1bklFAwi4iUpYr1/NOf7nPNyD+/J7krnnuW+L/fvY8xNAYaA2lpaVRMHe1fvmaK/6z2JSOJzVqFLSrgwr1z2VGpPbds+4ZbfeMoyTV8FPUXri4YztSEJym3JwMA3+5Mchd76e71z3e+10bimfMuP8yfy9LCqmyqfRHPZf7Ct+E9WZcXQe3w3Vy45kdY8yMAu3u9RvkzroWty+DHx/1fgytXkzUT3+GVzCac264JvVskYTbMgOrt/PO0ywlTMIuIBIO3lH9+63bz37e6Eo+1kJsF+buplNgANi+GtdPwVG3BdcldYUoDys39ADo8APFJtJ/4JL4SH7mRdYgq2Mbazi+ROP8V2ub+Rs+wqWRvGk8xXsZV+Au12jVmTeZqqufG8+jmVJ7xvknSd48xIm0ufX3jqViYyZjXDdlVUrg2819c6mvD/ctvoaDlTi7/9RHS47uSxA7Kd7uJ8I6DYNd6srdnkldQxA9LNpG4ZgzN4/cS0WEg5uBJXUqKYewdlNTpjqftAIz1+S9tehqP1BXMIiJOYIz/FlfZfwP/bvOqLdkfYd3u/93c6Cblr3h9JcQYAwXZNI9OgB5X+V/8/hHKzXgD+r7LGy0vDXyiKXAhw/YWkj67Jk2m3sRN+e+z2SawNLIlvQonsTxjJYXeCM7yLGBO1GDyV4ZTiJfmOVMpsQbvt/fy28zRJGdNoRw+4kua87OvNY+G+3eZT5r8A1n1L2NvVFU6rH2bqmE5VNy5iN0Lv+XxeZV5ufBxPNXbkpn6PPGbZxJfIRGqtiB95L8pMhG0ufygr9kBzHwb9mz2HyPP2QRNLzpJG+DUUTCLiLiVMQdG5n8827rn09DtPohN/NPHEmIj6JraG7pvgILdVI4oT9Udv2LfOovWxauxqY9C7TPwrZtB/rwvmZx8Nz0iV7A04WzWTHiL/lnfMi+8PQV1zqLzqhfp6k1nW2InRm6uwq1hX8OvMwDwYci3ESyydWnlWcPf1t+Gx7MVtqbz0Vy4zzucXVFJTE55jQsXPUcekbwQcTZJiZWwhXlc06Y8ZuKTULQXFn6O3bMFc/cCKB+4ZnhJkX8Sm/mf+Ce0GfTDn/dULP/Gf1LcoO8hPAYK9/ony9ln0yL4+i7Ck+8pk01SGgpmEZHTkTGHDOXf8XggOgEvQOXGmNtnwMLhmI43Q3QFPHW7UyH1AS4JvL0T0PHMs8je/Bttq9TBeMOZ9U09OtavSOW6Z3HOhk3Y4T+yNrwBkfEVqZrSh/SKF7JlbzGN591B0rYVTKh4Ow03juEh7ycUEUaF/I20m3ITPjzEmXyumt2PnTaOiiaHkp92EUaJ/4dnZ2CAr1+5m1rVq1Np+2zyPLHUzU/Hgw9vSQHbZn9BYpXq4A3H1Ons/9yMN2HzIpj3EawcD+t/gYHfQk4mpI/2f7Uucz5J0T8Dl5b9djgEBbOIiJROQjKkPnTEtxhjKFftwExvubG1oEkqAA0bNITbZlA3viqERwOwf5by1v4rnp0LkP8QLPsaT6WG+EbdSlL2JlamPEuztR+StH015cKjKDFxLC+qRm5YOSpFlFAxdzULYzpzcfYY2HCgnh02jhiKyaICid8NxhgLwMTqt7Ag5kzuXTsVD8D3D1FoIikKjyNyaC/CbOHv+orZkIbPZ/F4Tv6xbwWziIicOvtmdzuSqHLQ9mr/SP3GH/AYD81jKsKeSwm3lvC4KmAtCfsuoJK7A4rySI1Lovjr+yjevorILoMx21Zgm19P+vp1hK2bimfFh3wZN4C2OT9zTuZbnMNbAMwM70inolk8ZQexIKcGd4WPZnJJC87wruRCz3Rm0ZyOheksWjSHVm2OcsGTMqBgFhGR0HXw7va4KgceH3zW9kEXVAm79JUDwdb0YioBlSpVhrYpwD3cCuD7G0VrphG+bQmUr0mnOl1g7VSebHIRewpLiAy7nfDftjMnfQXeLWOYGnMuJm8MHRokn6wuf0fBLCIipxePh/D63aB+twPLml2CAeKj/KPw1MZVSG1cBehGLyAtLerA2fAnu7xT8lNERESkVBTMIiIiIUTBLCIiEkIUzCIiIiFEwSwiIhJCFMwiIiIhRMEsIiISQhTMIiIiIUTBLCIiEkIUzCIiIiFEwSwiIhJCFMwiIiIhRMEsIiISQoy1Ntg1YIzZBqwr49UmAtvLeJ3B4JY+QL2EKrf04pY+QL2EopPRRx1r7Z8uWRUSwXwyGGPmWGtTgl3HiXJLH6BeQpVbenFLH6BeQtGp7EO7skVEREKIgllERCSEuDmY3w52AWXELX2AeglVbunFLX2AeglFp6wP1x5jFhERcSI3j5hFREQcx3XBbIzpZYxZYYxZZYx5KNj1HCtjzFpjzGJjzAJjzJzAsorGmB+NMb8G7hOCXeehGGPeM8ZsNcYsOWjZYWs3xjwc2E4rjDE9g1P1nx2mjyeMMRsD22WBMab3Qa+FZB8AxphaxphJxphlxph0Y8zdgeWO2i5H6MNx28UYE2WMmWWMWRjo5Z+B5Y7aJnDEXhy3XQCMMV5jzHxjzLjA8+BsE2uta26AF/gNqAdEAAuBZsGu6xh7WAsk/mHZc8BDgccPAf8Jdp2Hqb070A5YcrTagWaB7RMJ1A1sN2+wezhCH08AfzvEe0O2j0B91YB2gcfxwMpAzY7aLkfow3HbBTBAXOBxODATOMNp2+QovThuuwTquw/4FBgXeB6UbeK2EXNHYJW1drW1thAYDvQJck1loQ/wYeDxh8ClwSvl8Ky1k4Edf1h8uNr7AMOttQXW2jXAKvzbL+gO08fhhGwfANbaTdbaeYHHOcAyoAYO2y5H6ONwQrIPAOu3J/A0PHCzOGybwBF7OZyQ7cUYUxO4EHj3oMVB2SZuC+YawIaDnmdw5P95Q5EFfjDGzDXG3BxYlmSt3QT+P1BAlaBVd+wOV7sTt9UdxphFgV3d+3ZpOaYPY0wy0Bb/qMax2+UPfYADt0tgl+kCYCvwo7XWsdvkML2A87bLS8ADgO+gZUHZJm4LZnOIZU477byLtbYdcAFwuzGme7ALOkmctq3eBOoDbYBNwAuB5Y7owxgTB4wE7rHWZh/prYdYFjL9HKIPR24Xa22JtbYNUBPoaIxpcYS3O7EXR20XY8xFwFZr7dzSfuQQy8qsD7cFcwZQ66DnNYHMINVyXKy1mYH7rcAo/LtHthhjqgEE7rcGr8JjdrjaHbWtrLVbAn+AfMA7HNhtFfJ9GGPC8YfZMGvtV4HFjtsuh+rDydsFwFq7C0gDeuHAbXKwg3tx4HbpAlxijFmL/xDo2caYTwjSNnFbMM8GGhpj6hpjIoD+wNgg11RqxphYY0z8vsfA+cAS/D1cH3jb9cCY4FR4XA5X+1igvzEm0hhTF2gIzApCfaWy73/OgMvwbxcI8T6MMQYYCiyz1g456CVHbZfD9eHE7WKMqWyMqRB4HA2cCyzHYdsEDt+L07aLtfZha21Na20y/tz4yVp7DcHaJsE+C66sb0Bv/Gds/gY8Gux6jrH2evjP9FsIpO+rH6gETAR+DdxXDHath6n/M/y7rYrw/4vyr0eqHXg0sJ1WABcEu/6j9PExsBhYFPifslqo9xGorSv+XWyLgAWBW2+nbZcj9OG47QK0AuYHal4C/COw3FHb5Ci9OG67HFRfKgfOyg7KNtHMXyIiIiHEbbuyRUREHE3BLCIiEkIUzCIiIiFEwSwiIhJCFMwiIiIhRMEsIkdkjEndd7UdETn5FMwiIiIhRMEs4hLGmGsC18ZdYIx5K3BxgT3GmBeMMfOMMRONMZUD721jjJkRuMjAqH0XGTDGNDDGTAhcX3eeMaZ+YPVxxpgvjTHLjTHDAjNxichJoGAWcQFjTFPgKvwXQWkDlABXA7HAPOu/MMrPwOOBj3wEPGitbYV/hqZ9y4cBr1trWwOd8c+ABv6rOd2D/zq09fDPLSwiJ0FYsAsQkTJxDtAemB0YzEbjn3DfB3weeM8nwFfGmPJABWvtz4HlHwJfBOZpr2GtHQVgrc0HCKxvlrU2I/B8AZAMTD3pXYmchhTMIu5ggA+ttQ//bqExj/3hfUeag/dIu6cLDnpcgv52iJw02pUt4g4TgSuMMVUAjDEVjTF18P8/fkXgPX8BplprdwM7jTHdAsuvBX62/usbZxhjLg2sI9IYE3MqmxAR/atXxBWstUuNMX8HfjDGePBfGet2YC/Q3BgzF9iN/zg0+C9h979A8K4GbggsvxZ4yxjzZGAd/U5hGyICurqUiJsZY/ZYa+OCXYeIlJ52ZYuIiIQQjZhFRERCiEbMIiIiIUTBLCIiEkIUzCIiIiFEwSwiIhJCFMwiIiIhRMEsIiISQv4fLu9gME3DNkEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/1000:  train Loss: 62.7761   val Loss: 62.3029   time: 1.30s   best: 62.3029\n",
      "Epoch 403/1000:  train Loss: 62.2635   val Loss: 62.9044   time: 1.30s   best: 62.3029\n",
      "Epoch 404/1000:  train Loss: 62.2018   val Loss: 63.1461   time: 1.30s   best: 62.3029\n",
      "Epoch 405/1000:  train Loss: 63.6190   val Loss: 63.4916   time: 1.31s   best: 62.3029\n",
      "Epoch 406/1000:  train Loss: 62.4117   val Loss: 62.0081   time: 1.34s   best: 62.0081\n",
      "Epoch 407/1000:  train Loss: 62.1703   val Loss: 62.4627   time: 1.34s   best: 62.0081\n",
      "Epoch 408/1000:  train Loss: 62.2741   val Loss: 63.7931   time: 1.34s   best: 62.0081\n",
      "Epoch 409/1000:  train Loss: 61.9314   val Loss: 62.1379   time: 1.35s   best: 62.0081\n",
      "Epoch 410/1000:  train Loss: 61.8566   val Loss: 62.7333   time: 1.34s   best: 62.0081\n",
      "Epoch 411/1000:  train Loss: 62.4425   val Loss: 62.9217   time: 1.33s   best: 62.0081\n",
      "Epoch 412/1000:  train Loss: 62.0144   val Loss: 62.6919   time: 1.32s   best: 62.0081\n",
      "Epoch 413/1000:  train Loss: 62.0150   val Loss: 63.6371   time: 1.31s   best: 62.0081\n",
      "Epoch 414/1000:  train Loss: 62.3553   val Loss: 62.0459   time: 1.31s   best: 62.0081\n",
      "Epoch 415/1000:  train Loss: 63.7811   val Loss: 62.1235   time: 1.31s   best: 62.0081\n",
      "Epoch 416/1000:  train Loss: 63.5261   val Loss: 62.4674   time: 1.30s   best: 62.0081\n",
      "Epoch 417/1000:  train Loss: 61.8550   val Loss: 61.8925   time: 1.30s   best: 61.8925\n",
      "Epoch 418/1000:  train Loss: 61.7966   val Loss: 61.6326   time: 1.29s   best: 61.6326\n",
      "Epoch 419/1000:  train Loss: 61.4391   val Loss: 62.7740   time: 1.30s   best: 61.6326\n",
      "Epoch 420/1000:  train Loss: 61.8134   val Loss: 61.5737   time: 1.35s   best: 61.5737\n",
      "Epoch 421/1000:  train Loss: 61.5723   val Loss: 61.7621   time: 1.34s   best: 61.5737\n",
      "Epoch 422/1000:  train Loss: 61.4706   val Loss: 62.1682   time: 1.34s   best: 61.5737\n",
      "Epoch 423/1000:  train Loss: 61.6279   val Loss: 62.4163   time: 1.34s   best: 61.5737\n",
      "Epoch 424/1000:  train Loss: 61.7094   val Loss: 62.1288   time: 1.33s   best: 61.5737\n",
      "Epoch 425/1000:  train Loss: 61.0620   val Loss: 61.5829   time: 1.33s   best: 61.5737\n",
      "Epoch 426/1000:  train Loss: 61.2099   val Loss: 62.4060   time: 1.32s   best: 61.5737\n",
      "Epoch 427/1000:  train Loss: 61.2522   val Loss: 63.3027   time: 1.31s   best: 61.5737\n",
      "Epoch 428/1000:  train Loss: 61.5249   val Loss: 61.7379   time: 1.30s   best: 61.5737\n",
      "Epoch 429/1000:  train Loss: 60.9616   val Loss: 61.2588   time: 1.31s   best: 61.2588\n",
      "Epoch 430/1000:  train Loss: 61.1668   val Loss: 62.5013   time: 1.30s   best: 61.2588\n",
      "Epoch 431/1000:  train Loss: 62.1240   val Loss: 61.3718   time: 1.30s   best: 61.2588\n",
      "Epoch 432/1000:  train Loss: 60.8194   val Loss: 61.2913   time: 1.31s   best: 61.2588\n",
      "Epoch 433/1000:  train Loss: 61.0850   val Loss: 61.2799   time: 1.31s   best: 61.2588\n",
      "Epoch 434/1000:  train Loss: 60.8312   val Loss: 62.3347   time: 1.30s   best: 61.2588\n",
      "Epoch 435/1000:  train Loss: 61.4037   val Loss: 62.2545   time: 1.33s   best: 61.2588\n",
      "Epoch 436/1000:  train Loss: 61.0047   val Loss: 60.6484   time: 1.34s   best: 60.6484\n",
      "Epoch 437/1000:  train Loss: 60.4503   val Loss: 62.2773   time: 1.34s   best: 60.6484\n",
      "Epoch 438/1000:  train Loss: 60.5592   val Loss: 61.0300   time: 1.34s   best: 60.6484\n",
      "Epoch 439/1000:  train Loss: 60.8603   val Loss: 61.5240   time: 1.33s   best: 60.6484\n",
      "Epoch 440/1000:  train Loss: 61.0313   val Loss: 61.6141   time: 1.37s   best: 60.6484\n",
      "Epoch 441/1000:  train Loss: 61.1617   val Loss: 61.3491   time: 1.42s   best: 60.6484\n",
      "Epoch 442/1000:  train Loss: 61.2658   val Loss: 61.7632   time: 1.31s   best: 60.6484\n",
      "Epoch 443/1000:  train Loss: 61.3067   val Loss: 61.2810   time: 1.30s   best: 60.6484\n",
      "Epoch 444/1000:  train Loss: 60.6534   val Loss: 61.2598   time: 1.30s   best: 60.6484\n",
      "Epoch 445/1000:  train Loss: 60.5607   val Loss: 61.8548   time: 1.32s   best: 60.6484\n",
      "Epoch 446/1000:  train Loss: 60.5064   val Loss: 61.7591   time: 1.30s   best: 60.6484\n",
      "Epoch 447/1000:  train Loss: 60.5797   val Loss: 62.3914   time: 1.30s   best: 60.6484\n",
      "Epoch 448/1000:  train Loss: 60.7504   val Loss: 62.2696   time: 1.28s   best: 60.6484\n",
      "Epoch 449/1000:  train Loss: 60.9298   val Loss: 62.2600   time: 1.27s   best: 60.6484\n",
      "Epoch 450/1000:  train Loss: 60.8891   val Loss: 61.5076   time: 1.29s   best: 60.6484\n",
      "Epoch 451/1000:  train Loss: 60.3974   val Loss: 61.4819   time: 1.31s   best: 60.6484\n",
      "Epoch 452/1000:  train Loss: 59.9724   val Loss: 61.2933   time: 1.32s   best: 60.6484\n",
      "Epoch 453/1000:  train Loss: 60.5234   val Loss: 61.9488   time: 1.32s   best: 60.6484\n",
      "Epoch 454/1000:  train Loss: 60.5873   val Loss: 60.6026   time: 1.31s   best: 60.6026\n",
      "Epoch 455/1000:  train Loss: 60.1219   val Loss: 61.0762   time: 1.31s   best: 60.6026\n",
      "Epoch 456/1000:  train Loss: 60.2786   val Loss: 61.1394   time: 1.28s   best: 60.6026\n",
      "Epoch 457/1000:  train Loss: 60.4374   val Loss: 60.5462   time: 1.20s   best: 60.5462\n",
      "Epoch 458/1000:  train Loss: 60.2918   val Loss: 60.9607   time: 1.18s   best: 60.5462\n",
      "Epoch 459/1000:  train Loss: 60.1715   val Loss: 60.9793   time: 1.19s   best: 60.5462\n",
      "Epoch 460/1000:  train Loss: 59.9085   val Loss: 61.4535   time: 1.18s   best: 60.5462\n",
      "Epoch 461/1000:  train Loss: 61.0711   val Loss: 61.2493   time: 1.18s   best: 60.5462\n",
      "Epoch 462/1000:  train Loss: 60.0678   val Loss: 61.3029   time: 1.18s   best: 60.5462\n",
      "Epoch 463/1000:  train Loss: 61.4703   val Loss: 63.4273   time: 1.18s   best: 60.5462\n",
      "Epoch 464/1000:  train Loss: 60.9579   val Loss: 60.9163   time: 1.17s   best: 60.5462\n",
      "Epoch 465/1000:  train Loss: 59.9649   val Loss: 60.6440   time: 1.17s   best: 60.5462\n",
      "Epoch 466/1000:  train Loss: 59.7814   val Loss: 60.8079   time: 1.20s   best: 60.5462\n",
      "Epoch 467/1000:  train Loss: 59.8738   val Loss: 61.4299   time: 1.22s   best: 60.5462\n",
      "Epoch 468/1000:  train Loss: 59.9697   val Loss: 61.0203   time: 1.21s   best: 60.5462\n",
      "Epoch 469/1000:  train Loss: 59.8713   val Loss: 61.1193   time: 1.24s   best: 60.5462\n",
      "Epoch 470/1000:  train Loss: 59.5487   val Loss: 61.6639   time: 1.33s   best: 60.5462\n",
      "Epoch 471/1000:  train Loss: 59.9031   val Loss: 61.0523   time: 1.31s   best: 60.5462\n",
      "Epoch 472/1000:  train Loss: 59.5006   val Loss: 61.2987   time: 1.31s   best: 60.5462\n",
      "Epoch 473/1000:  train Loss: 59.3541   val Loss: 61.2092   time: 1.28s   best: 60.5462\n",
      "Epoch 474/1000:  train Loss: 59.2887   val Loss: 60.2572   time: 1.23s   best: 60.2572\n",
      "Epoch 475/1000:  train Loss: 59.5889   val Loss: 61.3164   time: 1.21s   best: 60.2572\n",
      "Epoch 476/1000:  train Loss: 59.8255   val Loss: 61.5659   time: 1.19s   best: 60.2572\n",
      "Epoch 477/1000:  train Loss: 59.6711   val Loss: 61.3294   time: 1.18s   best: 60.2572\n",
      "Epoch 478/1000:  train Loss: 59.5325   val Loss: 60.9147   time: 1.18s   best: 60.2572\n",
      "Epoch 479/1000:  train Loss: 59.4105   val Loss: 60.8389   time: 1.17s   best: 60.2572\n",
      "Epoch 480/1000:  train Loss: 59.4718   val Loss: 60.1759   time: 1.17s   best: 60.1759\n",
      "Epoch 481/1000:  train Loss: 59.0023   val Loss: 60.2229   time: 1.18s   best: 60.1759\n",
      "Epoch 482/1000:  train Loss: 59.4733   val Loss: 60.6004   time: 1.20s   best: 60.1759\n",
      "Epoch 483/1000:  train Loss: 59.4862   val Loss: 61.0136   time: 1.21s   best: 60.1759\n",
      "Epoch 484/1000:  train Loss: 59.6526   val Loss: 60.9669   time: 1.21s   best: 60.1759\n",
      "Epoch 485/1000:  train Loss: 59.3616   val Loss: 59.9379   time: 1.21s   best: 59.9379\n",
      "Epoch 486/1000:  train Loss: 59.0680   val Loss: 61.0457   time: 1.21s   best: 59.9379\n",
      "Epoch 487/1000:  train Loss: 59.6145   val Loss: 61.1256   time: 1.21s   best: 59.9379\n",
      "Epoch 488/1000:  train Loss: 59.5377   val Loss: 60.0339   time: 1.21s   best: 59.9379\n",
      "Epoch 489/1000:  train Loss: 58.7804   val Loss: 60.2270   time: 1.19s   best: 59.9379\n",
      "Epoch 490/1000:  train Loss: 58.7957   val Loss: 60.6467   time: 1.18s   best: 59.9379\n",
      "Epoch 491/1000:  train Loss: 60.3283   val Loss: 60.4024   time: 1.19s   best: 59.9379\n",
      "Epoch 492/1000:  train Loss: 59.1171   val Loss: 59.8974   time: 1.19s   best: 59.8974\n",
      "Epoch 493/1000:  train Loss: 58.8654   val Loss: 60.0988   time: 1.19s   best: 59.8974\n",
      "Epoch 494/1000:  train Loss: 58.6794   val Loss: 60.9870   time: 1.18s   best: 59.8974\n",
      "Epoch 495/1000:  train Loss: 59.2535   val Loss: 60.3221   time: 1.18s   best: 59.8974\n",
      "Epoch 496/1000:  train Loss: 58.7566   val Loss: 60.3470   time: 1.19s   best: 59.8974\n",
      "Epoch 497/1000:  train Loss: 58.7215   val Loss: 60.4862   time: 1.18s   best: 59.8974\n",
      "Epoch 498/1000:  train Loss: 58.5822   val Loss: 59.9900   time: 1.20s   best: 59.8974\n",
      "Epoch 499/1000:  train Loss: 58.7367   val Loss: 60.1638   time: 1.21s   best: 59.8974\n",
      "Epoch 500/1000:  train Loss: 58.5251   val Loss: 59.8815   time: 1.21s   best: 59.8815\n",
      "Epoch 501/1000:  train Loss: 58.6094   val Loss: 60.1851   time: 1.21s   best: 59.8815\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 502/1000:  train Loss: 58.9162   val Loss: 60.0999   time: 1.21s   best: 59.8815\n",
      "Epoch 503/1000:  train Loss: 58.8949   val Loss: 60.3698   time: 1.21s   best: 59.8815\n",
      "Epoch 504/1000:  train Loss: 58.9288   val Loss: 60.0446   time: 1.21s   best: 59.8815\n",
      "Epoch 505/1000:  train Loss: 58.3642   val Loss: 60.5299   time: 1.19s   best: 59.8815\n",
      "Epoch 506/1000:  train Loss: 58.4570   val Loss: 60.6071   time: 1.19s   best: 59.8815\n",
      "Epoch 507/1000:  train Loss: 58.5188   val Loss: 59.5193   time: 1.19s   best: 59.5193\n",
      "Epoch 508/1000:  train Loss: 58.4625   val Loss: 60.3700   time: 1.19s   best: 59.5193\n",
      "Epoch 509/1000:  train Loss: 58.2240   val Loss: 59.9698   time: 1.18s   best: 59.5193\n",
      "Epoch 510/1000:  train Loss: 58.1507   val Loss: 60.0343   time: 1.18s   best: 59.5193\n",
      "Epoch 511/1000:  train Loss: 58.1786   val Loss: 59.9387   time: 1.18s   best: 59.5193\n",
      "Epoch 512/1000:  train Loss: 58.2031   val Loss: 60.1160   time: 1.18s   best: 59.5193\n",
      "Epoch 513/1000:  train Loss: 57.9971   val Loss: 59.8354   time: 1.17s   best: 59.5193\n",
      "Epoch 514/1000:  train Loss: 58.2375   val Loss: 59.9005   time: 1.19s   best: 59.5193\n",
      "Epoch 515/1000:  train Loss: 58.2050   val Loss: 59.8879   time: 1.20s   best: 59.5193\n",
      "Epoch 516/1000:  train Loss: 58.9203   val Loss: 59.5742   time: 1.21s   best: 59.5193\n",
      "Epoch 517/1000:  train Loss: 59.6013   val Loss: 59.6372   time: 1.22s   best: 59.5193\n",
      "Epoch 518/1000:  train Loss: 57.7026   val Loss: 59.7045   time: 1.21s   best: 59.5193\n",
      "Epoch 519/1000:  train Loss: 57.8649   val Loss: 59.6207   time: 1.21s   best: 59.5193\n",
      "Epoch 520/1000:  train Loss: 57.9489   val Loss: 59.5618   time: 1.21s   best: 59.5193\n",
      "Epoch 521/1000:  train Loss: 58.4298   val Loss: 59.2398   time: 1.21s   best: 59.2398\n",
      "Epoch 522/1000:  train Loss: 57.8915   val Loss: 59.4381   time: 1.19s   best: 59.2398\n",
      "Epoch 523/1000:  train Loss: 58.0076   val Loss: 60.8926   time: 1.19s   best: 59.2398\n",
      "Epoch 524/1000:  train Loss: 58.3290   val Loss: 60.1447   time: 1.19s   best: 59.2398\n",
      "Epoch 525/1000:  train Loss: 58.3713   val Loss: 60.0756   time: 1.19s   best: 59.2398\n",
      "Epoch 526/1000:  train Loss: 57.8838   val Loss: 58.9287   time: 1.19s   best: 58.9287\n",
      "Epoch 527/1000:  train Loss: 57.4665   val Loss: 59.6379   time: 1.18s   best: 58.9287\n",
      "Epoch 528/1000:  train Loss: 57.5901   val Loss: 59.5305   time: 1.18s   best: 58.9287\n",
      "Epoch 529/1000:  train Loss: 57.7210   val Loss: 59.7694   time: 1.17s   best: 58.9287\n",
      "Epoch 530/1000:  train Loss: 57.5470   val Loss: 59.6975   time: 1.19s   best: 58.9287\n",
      "Epoch 531/1000:  train Loss: 58.0588   val Loss: 59.9907   time: 1.21s   best: 58.9287\n",
      "Epoch 532/1000:  train Loss: 58.2984   val Loss: 59.5999   time: 1.21s   best: 58.9287\n",
      "Epoch 533/1000:  train Loss: 57.5847   val Loss: 59.0490   time: 1.22s   best: 58.9287\n",
      "Epoch 534/1000:  train Loss: 57.5811   val Loss: 59.2447   time: 1.22s   best: 58.9287\n",
      "Epoch 535/1000:  train Loss: 57.4840   val Loss: 59.2260   time: 1.21s   best: 58.9287\n",
      "Epoch 536/1000:  train Loss: 57.4353   val Loss: 59.0985   time: 1.21s   best: 58.9287\n",
      "Epoch 537/1000:  train Loss: 57.2173   val Loss: 59.3988   time: 1.20s   best: 58.9287\n",
      "Epoch 538/1000:  train Loss: 57.6562   val Loss: 59.1638   time: 1.18s   best: 58.9287\n",
      "Epoch 539/1000:  train Loss: 57.1959   val Loss: 59.4231   time: 1.19s   best: 58.9287\n",
      "Epoch 540/1000:  train Loss: 57.3860   val Loss: 59.2538   time: 1.19s   best: 58.9287\n",
      "Epoch 541/1000:  train Loss: 57.2189   val Loss: 59.3900   time: 1.19s   best: 58.9287\n",
      "Epoch 542/1000:  train Loss: 57.5059   val Loss: 59.3518   time: 1.18s   best: 58.9287\n",
      "Epoch 543/1000:  train Loss: 57.1339   val Loss: 59.9804   time: 1.18s   best: 58.9287\n",
      "Epoch 544/1000:  train Loss: 57.5115   val Loss: 59.2244   time: 1.18s   best: 58.9287\n",
      "Epoch 545/1000:  train Loss: 57.4746   val Loss: 59.6342   time: 1.17s   best: 58.9287\n",
      "Epoch 546/1000:  train Loss: 57.0333   val Loss: 58.8480   time: 1.18s   best: 58.8480\n",
      "Epoch 547/1000:  train Loss: 57.0000   val Loss: 58.7656   time: 1.19s   best: 58.7656\n",
      "Epoch 548/1000:  train Loss: 56.7750   val Loss: 58.6565   time: 1.21s   best: 58.6565\n",
      "Epoch 549/1000:  train Loss: 56.8906   val Loss: 60.0343   time: 1.22s   best: 58.6565\n",
      "Epoch 550/1000:  train Loss: 56.9401   val Loss: 58.9595   time: 1.21s   best: 58.6565\n",
      "Epoch 551/1000:  train Loss: 56.7324   val Loss: 58.8429   time: 1.36s   best: 58.6565\n",
      "Epoch 552/1000:  train Loss: 56.6827   val Loss: 58.6605   time: 1.32s   best: 58.6565\n",
      "Epoch 553/1000:  train Loss: 57.2810   val Loss: 58.9854   time: 1.32s   best: 58.6565\n",
      "Epoch 554/1000:  train Loss: 57.1919   val Loss: 58.6615   time: 1.30s   best: 58.6565\n",
      "Epoch 555/1000:  train Loss: 56.6336   val Loss: 59.4848   time: 1.27s   best: 58.6565\n",
      "Epoch 556/1000:  train Loss: 56.5783   val Loss: 58.3461   time: 1.21s   best: 58.3461\n",
      "Epoch 557/1000:  train Loss: 56.7462   val Loss: 58.9589   time: 1.19s   best: 58.3461\n",
      "Epoch 558/1000:  train Loss: 56.7461   val Loss: 58.4181   time: 1.18s   best: 58.3461\n",
      "Epoch 559/1000:  train Loss: 56.6893   val Loss: 58.5032   time: 1.18s   best: 58.3461\n",
      "Epoch 560/1000:  train Loss: 56.6554   val Loss: 58.4352   time: 1.18s   best: 58.3461\n",
      "Epoch 561/1000:  train Loss: 56.5202   val Loss: 58.8250   time: 1.17s   best: 58.3461\n",
      "Epoch 562/1000:  train Loss: 56.7097   val Loss: 60.1145   time: 1.20s   best: 58.3461\n",
      "Epoch 563/1000:  train Loss: 57.5090   val Loss: 58.1935   time: 1.22s   best: 58.1935\n",
      "Epoch 564/1000:  train Loss: 56.2111   val Loss: 58.7596   time: 1.22s   best: 58.1935\n",
      "Epoch 565/1000:  train Loss: 56.3138   val Loss: 58.6876   time: 1.22s   best: 58.1935\n",
      "Epoch 566/1000:  train Loss: 56.2458   val Loss: 58.8287   time: 1.21s   best: 58.1935\n",
      "Epoch 567/1000:  train Loss: 56.2554   val Loss: 58.5509   time: 1.21s   best: 58.1935\n",
      "Epoch 568/1000:  train Loss: 56.0941   val Loss: 58.1764   time: 1.21s   best: 58.1764\n",
      "Epoch 569/1000:  train Loss: 56.2251   val Loss: 58.3115   time: 1.20s   best: 58.1764\n",
      "Epoch 570/1000:  train Loss: 56.0155   val Loss: 58.3421   time: 1.19s   best: 58.1764\n",
      "Epoch 571/1000:  train Loss: 56.4494   val Loss: 58.5872   time: 1.19s   best: 58.1764\n",
      "Epoch 572/1000:  train Loss: 56.3461   val Loss: 58.7513   time: 1.19s   best: 58.1764\n",
      "Epoch 573/1000:  train Loss: 56.4260   val Loss: 58.7971   time: 1.19s   best: 58.1764\n",
      "Epoch 574/1000:  train Loss: 56.4415   val Loss: 58.6017   time: 1.19s   best: 58.1764\n",
      "Epoch 575/1000:  train Loss: 56.4516   val Loss: 58.6422   time: 1.18s   best: 58.1764\n",
      "Epoch 576/1000:  train Loss: 56.3288   val Loss: 58.4666   time: 1.17s   best: 58.1764\n",
      "Epoch 577/1000:  train Loss: 55.9460   val Loss: 58.6868   time: 1.18s   best: 58.1764\n",
      "Epoch 578/1000:  train Loss: 55.7445   val Loss: 57.9233   time: 1.18s   best: 57.9233\n",
      "Epoch 579/1000:  train Loss: 55.5733   val Loss: 58.5028   time: 1.20s   best: 57.9233\n",
      "Epoch 580/1000:  train Loss: 56.1707   val Loss: 58.3932   time: 1.21s   best: 57.9233\n",
      "Epoch 581/1000:  train Loss: 62.4907   val Loss: 59.3229   time: 1.22s   best: 57.9233\n",
      "Epoch 582/1000:  train Loss: 56.5899   val Loss: 58.4455   time: 1.21s   best: 57.9233\n",
      "Epoch 583/1000:  train Loss: 56.1416   val Loss: 58.4457   time: 1.22s   best: 57.9233\n",
      "Epoch 584/1000:  train Loss: 55.9412   val Loss: 57.6602   time: 1.21s   best: 57.6602\n",
      "Epoch 585/1000:  train Loss: 55.7102   val Loss: 58.0956   time: 1.21s   best: 57.6602\n",
      "Epoch 586/1000:  train Loss: 55.5705   val Loss: 57.7491   time: 1.19s   best: 57.6602\n",
      "Epoch 587/1000:  train Loss: 55.4845   val Loss: 57.8480   time: 1.19s   best: 57.6602\n",
      "Epoch 588/1000:  train Loss: 55.6727   val Loss: 57.8724   time: 1.19s   best: 57.6602\n",
      "Epoch 589/1000:  train Loss: 55.5182   val Loss: 58.3848   time: 1.18s   best: 57.6602\n",
      "Epoch 590/1000:  train Loss: 55.6654   val Loss: 57.5267   time: 1.18s   best: 57.5267\n",
      "Epoch 591/1000:  train Loss: 55.3951   val Loss: 58.1806   time: 1.18s   best: 57.5267\n",
      "Epoch 592/1000:  train Loss: 55.4065   val Loss: 58.4417   time: 1.18s   best: 57.5267\n",
      "Epoch 593/1000:  train Loss: 55.5332   val Loss: 57.9605   time: 1.17s   best: 57.5267\n",
      "Epoch 594/1000:  train Loss: 55.3506   val Loss: 57.6253   time: 1.18s   best: 57.5267\n",
      "Epoch 595/1000:  train Loss: 55.0903   val Loss: 57.6230   time: 1.21s   best: 57.5267\n",
      "Epoch 596/1000:  train Loss: 54.9070   val Loss: 57.4955   time: 1.21s   best: 57.4955\n",
      "Epoch 597/1000:  train Loss: 55.0763   val Loss: 57.9901   time: 1.21s   best: 57.4955\n",
      "Epoch 598/1000:  train Loss: 55.4199   val Loss: 57.9465   time: 1.21s   best: 57.4955\n",
      "Epoch 599/1000:  train Loss: 55.1085   val Loss: 57.4516   time: 1.21s   best: 57.4516\n",
      "Epoch 600/1000:  train Loss: 55.1014   val Loss: 58.3187   time: 1.21s   best: 57.4516\n",
      "Epoch 601/1000:  train Loss: 55.5327   val Loss: 57.8768   time: 1.20s   best: 57.4516\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 602/1000:  train Loss: 55.4625   val Loss: 57.6223   time: 1.19s   best: 57.4516\n",
      "Epoch 603/1000:  train Loss: 55.3622   val Loss: 57.3626   time: 1.18s   best: 57.3626\n",
      "Epoch 604/1000:  train Loss: 54.8956   val Loss: 57.9047   time: 1.18s   best: 57.3626\n",
      "Epoch 605/1000:  train Loss: 56.5570   val Loss: 57.7143   time: 1.19s   best: 57.3626\n",
      "Epoch 606/1000:  train Loss: 54.9881   val Loss: 58.3616   time: 1.18s   best: 57.3626\n",
      "Epoch 607/1000:  train Loss: 55.5259   val Loss: 57.6038   time: 1.18s   best: 57.3626\n",
      "Epoch 608/1000:  train Loss: 54.9544   val Loss: 57.2165   time: 1.18s   best: 57.2165\n",
      "Epoch 609/1000:  train Loss: 54.6776   val Loss: 57.2996   time: 1.18s   best: 57.2165\n",
      "Epoch 610/1000:  train Loss: 54.8592   val Loss: 57.4335   time: 1.17s   best: 57.2165\n",
      "Epoch 611/1000:  train Loss: 54.6894   val Loss: 57.1551   time: 1.19s   best: 57.1551\n",
      "Epoch 612/1000:  train Loss: 55.0708   val Loss: 57.3360   time: 1.20s   best: 57.1551\n",
      "Epoch 613/1000:  train Loss: 54.8733   val Loss: 56.9843   time: 1.21s   best: 56.9843\n",
      "Epoch 614/1000:  train Loss: 54.5434   val Loss: 57.1931   time: 1.22s   best: 56.9843\n",
      "Epoch 615/1000:  train Loss: 54.8947   val Loss: 57.3059   time: 1.21s   best: 56.9843\n",
      "Epoch 616/1000:  train Loss: 54.5962   val Loss: 57.3271   time: 1.21s   best: 56.9843\n",
      "Epoch 617/1000:  train Loss: 55.2143   val Loss: 57.3154   time: 1.21s   best: 56.9843\n",
      "Epoch 618/1000:  train Loss: 54.4488   val Loss: 57.0422   time: 1.21s   best: 56.9843\n",
      "Epoch 619/1000:  train Loss: 54.7023   val Loss: 56.8529   time: 1.19s   best: 56.8529\n",
      "Epoch 620/1000:  train Loss: 54.6948   val Loss: 56.7736   time: 1.19s   best: 56.7736\n",
      "Epoch 621/1000:  train Loss: 54.3956   val Loss: 57.6961   time: 1.19s   best: 56.7736\n",
      "Epoch 622/1000:  train Loss: 54.8370   val Loss: 57.1355   time: 1.18s   best: 56.7736\n",
      "Epoch 623/1000:  train Loss: 54.9271   val Loss: 57.1253   time: 1.18s   best: 56.7736\n",
      "Epoch 624/1000:  train Loss: 54.3911   val Loss: 57.4027   time: 1.18s   best: 56.7736\n",
      "Epoch 625/1000:  train Loss: 54.2500   val Loss: 57.3101   time: 1.18s   best: 56.7736\n",
      "Epoch 626/1000:  train Loss: 54.6443   val Loss: 56.7971   time: 1.18s   best: 56.7736\n",
      "Epoch 627/1000:  train Loss: 54.1063   val Loss: 56.7462   time: 1.18s   best: 56.7462\n",
      "Epoch 628/1000:  train Loss: 54.6108   val Loss: 56.9113   time: 1.20s   best: 56.7462\n",
      "Epoch 629/1000:  train Loss: 54.2632   val Loss: 56.4312   time: 1.22s   best: 56.4312\n",
      "Epoch 630/1000:  train Loss: 54.3561   val Loss: 57.4709   time: 1.21s   best: 56.4312\n",
      "Epoch 631/1000:  train Loss: 54.2632   val Loss: 56.9190   time: 1.21s   best: 56.4312\n",
      "Epoch 632/1000:  train Loss: 54.0488   val Loss: 57.2930   time: 1.22s   best: 56.4312\n",
      "Epoch 633/1000:  train Loss: 54.1327   val Loss: 56.7536   time: 1.28s   best: 56.4312\n",
      "Epoch 634/1000:  train Loss: 53.9785   val Loss: 57.5074   time: 1.22s   best: 56.4312\n",
      "Epoch 635/1000:  train Loss: 54.8505   val Loss: 57.9333   time: 1.25s   best: 56.4312\n",
      "Epoch 636/1000:  train Loss: 54.9379   val Loss: 57.0041   time: 1.20s   best: 56.4312\n",
      "Epoch 637/1000:  train Loss: 54.4054   val Loss: 56.7366   time: 1.20s   best: 56.4312\n",
      "Epoch 638/1000:  train Loss: 54.0941   val Loss: 56.4923   time: 1.21s   best: 56.4312\n",
      "Epoch 639/1000:  train Loss: 53.8412   val Loss: 57.0157   time: 1.30s   best: 56.4312\n",
      "Epoch 640/1000:  train Loss: 53.8584   val Loss: 56.7891   time: 1.28s   best: 56.4312\n",
      "Epoch 641/1000:  train Loss: 53.9915   val Loss: 56.8356   time: 1.28s   best: 56.4312\n",
      "Epoch 642/1000:  train Loss: 53.7391   val Loss: 57.1225   time: 1.28s   best: 56.4312\n",
      "Epoch 643/1000:  train Loss: 54.0879   val Loss: 56.4331   time: 1.28s   best: 56.4312\n",
      "Epoch 644/1000:  train Loss: 53.9093   val Loss: 57.5699   time: 1.31s   best: 56.4312\n",
      "Epoch 645/1000:  train Loss: 54.0488   val Loss: 56.7758   time: 1.33s   best: 56.4312\n",
      "Epoch 646/1000:  train Loss: 53.5804   val Loss: 56.8627   time: 1.32s   best: 56.4312\n",
      "Epoch 647/1000:  train Loss: 53.6272   val Loss: 57.2745   time: 1.32s   best: 56.4312\n",
      "Epoch 648/1000:  train Loss: 53.8639   val Loss: 56.2618   time: 1.32s   best: 56.2618\n",
      "Epoch 649/1000:  train Loss: 53.8542   val Loss: 56.4519   time: 1.32s   best: 56.2618\n",
      "Epoch 650/1000:  train Loss: 54.0775   val Loss: 56.8109   time: 1.31s   best: 56.2618\n",
      "Epoch 651/1000:  train Loss: 53.8203   val Loss: 56.6628   time: 1.29s   best: 56.2618\n",
      "Epoch 652/1000:  train Loss: 53.9558   val Loss: 56.1357   time: 1.29s   best: 56.1357\n",
      "Epoch 653/1000:  train Loss: 53.4561   val Loss: 56.5261   time: 1.29s   best: 56.1357\n",
      "Epoch 654/1000:  train Loss: 53.3714   val Loss: 56.3133   time: 1.28s   best: 56.1357\n",
      "Epoch 655/1000:  train Loss: 53.1989   val Loss: 56.7519   time: 1.28s   best: 56.1357\n",
      "Epoch 656/1000:  train Loss: 53.8888   val Loss: 56.7153   time: 1.28s   best: 56.1357\n",
      "Epoch 657/1000:  train Loss: 53.7901   val Loss: 57.7001   time: 1.28s   best: 56.1357\n",
      "Epoch 658/1000:  train Loss: 53.2981   val Loss: 56.6440   time: 1.28s   best: 56.1357\n",
      "Epoch 659/1000:  train Loss: 53.6360   val Loss: 56.1323   time: 1.30s   best: 56.1323\n",
      "Epoch 660/1000:  train Loss: 53.3219   val Loss: 55.8040   time: 1.32s   best: 55.8040\n",
      "Epoch 661/1000:  train Loss: 53.0805   val Loss: 55.9410   time: 1.31s   best: 55.8040\n",
      "Epoch 662/1000:  train Loss: 53.2187   val Loss: 55.8797   time: 1.32s   best: 55.8040\n",
      "Epoch 663/1000:  train Loss: 53.3833   val Loss: 56.8466   time: 1.32s   best: 55.8040\n",
      "Epoch 664/1000:  train Loss: 53.0067   val Loss: 56.3367   time: 1.32s   best: 55.8040\n",
      "Epoch 665/1000:  train Loss: 53.0922   val Loss: 56.5081   time: 1.31s   best: 55.8040\n",
      "Epoch 666/1000:  train Loss: 52.9537   val Loss: 56.0164   time: 1.28s   best: 55.8040\n",
      "Epoch 667/1000:  train Loss: 53.2830   val Loss: 56.3665   time: 1.29s   best: 55.8040\n",
      "Epoch 668/1000:  train Loss: 53.2670   val Loss: 56.1704   time: 1.29s   best: 55.8040\n",
      "Epoch 669/1000:  train Loss: 53.0960   val Loss: 56.5070   time: 1.28s   best: 55.8040\n",
      "Epoch 670/1000:  train Loss: 53.2285   val Loss: 56.3720   time: 1.28s   best: 55.8040\n",
      "Epoch 671/1000:  train Loss: 53.3658   val Loss: 56.0717   time: 1.27s   best: 55.8040\n",
      "Epoch 672/1000:  train Loss: 53.0656   val Loss: 56.1787   time: 1.28s   best: 55.8040\n",
      "Epoch 673/1000:  train Loss: 53.1195   val Loss: 55.8178   time: 1.28s   best: 55.8040\n",
      "Epoch 674/1000:  train Loss: 52.7926   val Loss: 56.1835   time: 1.30s   best: 55.8040\n",
      "Epoch 675/1000:  train Loss: 52.8414   val Loss: 56.4133   time: 1.33s   best: 55.8040\n",
      "Epoch 676/1000:  train Loss: 53.0399   val Loss: 56.2882   time: 1.32s   best: 55.8040\n",
      "Epoch 677/1000:  train Loss: 52.9488   val Loss: 55.9909   time: 1.32s   best: 55.8040\n",
      "Epoch 678/1000:  train Loss: 52.7901   val Loss: 55.8471   time: 1.32s   best: 55.8040\n",
      "Epoch 679/1000:  train Loss: 52.8808   val Loss: 56.2303   time: 1.32s   best: 55.8040\n",
      "Epoch 680/1000:  train Loss: 52.6691   val Loss: 55.9894   time: 1.31s   best: 55.8040\n",
      "Epoch 681/1000:  train Loss: 52.8528   val Loss: 56.0503   time: 1.30s   best: 55.8040\n",
      "Epoch 682/1000:  train Loss: 52.8027   val Loss: 55.9539   time: 1.29s   best: 55.8040\n",
      "Epoch 683/1000:  train Loss: 52.7556   val Loss: 55.8814   time: 1.29s   best: 55.8040\n",
      "Epoch 684/1000:  train Loss: 52.8104   val Loss: 55.5945   time: 1.29s   best: 55.5945\n",
      "Epoch 685/1000:  train Loss: 52.9765   val Loss: 56.0851   time: 1.28s   best: 55.5945\n",
      "Epoch 686/1000:  train Loss: 53.1892   val Loss: 55.9008   time: 1.28s   best: 55.5945\n",
      "Epoch 687/1000:  train Loss: 52.6731   val Loss: 55.9996   time: 1.28s   best: 55.5945\n",
      "Epoch 688/1000:  train Loss: 52.4446   val Loss: 55.6131   time: 1.28s   best: 55.5945\n",
      "Epoch 689/1000:  train Loss: 52.2761   val Loss: 55.9942   time: 1.29s   best: 55.5945\n",
      "Epoch 690/1000:  train Loss: 52.5925   val Loss: 55.4663   time: 1.32s   best: 55.4663\n",
      "Epoch 691/1000:  train Loss: 52.1189   val Loss: 55.6113   time: 1.32s   best: 55.4663\n",
      "Epoch 692/1000:  train Loss: 52.0488   val Loss: 56.3763   time: 1.32s   best: 55.4663\n",
      "Epoch 693/1000:  train Loss: 52.4742   val Loss: 55.4961   time: 1.32s   best: 55.4663\n",
      "Epoch 694/1000:  train Loss: 52.2491   val Loss: 55.3766   time: 1.32s   best: 55.3766\n",
      "Epoch 695/1000:  train Loss: 52.4277   val Loss: 56.2660   time: 1.31s   best: 55.3766\n",
      "Epoch 696/1000:  train Loss: 53.4405   val Loss: 56.5501   time: 1.29s   best: 55.3766\n",
      "Epoch 697/1000:  train Loss: 52.9637   val Loss: 55.8302   time: 1.28s   best: 55.3766\n",
      "Epoch 698/1000:  train Loss: 52.1303   val Loss: 55.3188   time: 1.29s   best: 55.3188\n",
      "Epoch 699/1000:  train Loss: 52.1187   val Loss: 55.4012   time: 1.29s   best: 55.3188\n",
      "Epoch 700/1000:  train Loss: 52.0963   val Loss: 55.1624   time: 1.29s   best: 55.1624\n",
      "Epoch 701/1000:  train Loss: 51.9342   val Loss: 55.2104   time: 1.28s   best: 55.1624\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 702/1000:  train Loss: 51.8863   val Loss: 55.6769   time: 1.28s   best: 55.1624\n",
      "Epoch 703/1000:  train Loss: 52.1399   val Loss: 55.2175   time: 1.27s   best: 55.1624\n",
      "Epoch 704/1000:  train Loss: 51.9678   val Loss: 55.2254   time: 1.28s   best: 55.1624\n",
      "Epoch 705/1000:  train Loss: 52.0451   val Loss: 54.8915   time: 1.31s   best: 54.8915\n",
      "Epoch 706/1000:  train Loss: 51.7811   val Loss: 55.2695   time: 1.33s   best: 54.8915\n",
      "Epoch 707/1000:  train Loss: 51.9750   val Loss: 55.5953   time: 1.32s   best: 54.8915\n",
      "Epoch 708/1000:  train Loss: 52.2306   val Loss: 55.5315   time: 1.25s   best: 54.8915\n",
      "Epoch 709/1000:  train Loss: 52.1252   val Loss: 55.2238   time: 1.27s   best: 54.8915\n",
      "Epoch 710/1000:  train Loss: 52.1010   val Loss: 55.3248   time: 1.36s   best: 54.8915\n",
      "Epoch 711/1000:  train Loss: 51.7429   val Loss: 55.7197   time: 1.38s   best: 54.8915\n",
      "Epoch 712/1000:  train Loss: 51.9930   val Loss: 55.3991   time: 1.32s   best: 54.8915\n",
      "Epoch 713/1000:  train Loss: 51.8220   val Loss: 55.2113   time: 1.26s   best: 54.8915\n",
      "Epoch 714/1000:  train Loss: 52.0413   val Loss: 55.5799   time: 1.30s   best: 54.8915\n",
      "Epoch 715/1000:  train Loss: 51.8394   val Loss: 54.9943   time: 1.30s   best: 54.8915\n",
      "Epoch 716/1000:  train Loss: 51.3914   val Loss: 55.4835   time: 1.30s   best: 54.8915\n",
      "Epoch 717/1000:  train Loss: 51.6101   val Loss: 55.9060   time: 1.29s   best: 54.8915\n",
      "Epoch 718/1000:  train Loss: 51.6958   val Loss: 55.4000   time: 1.28s   best: 54.8915\n",
      "Epoch 719/1000:  train Loss: 51.6422   val Loss: 55.1789   time: 1.28s   best: 54.8915\n",
      "Epoch 720/1000:  train Loss: 51.5248   val Loss: 55.4663   time: 1.31s   best: 54.8915\n",
      "Epoch 721/1000:  train Loss: 51.6595   val Loss: 55.3031   time: 1.32s   best: 54.8915\n",
      "Epoch 722/1000:  train Loss: 51.6507   val Loss: 54.9114   time: 1.33s   best: 54.8915\n",
      "Epoch 723/1000:  train Loss: 52.0654   val Loss: 55.3672   time: 1.33s   best: 54.8915\n",
      "Epoch 724/1000:  train Loss: 51.6014   val Loss: 55.4664   time: 1.32s   best: 54.8915\n",
      "Epoch 725/1000:  train Loss: 51.3179   val Loss: 54.9021   time: 1.32s   best: 54.8915\n",
      "Epoch 726/1000:  train Loss: 51.4083   val Loss: 55.2618   time: 1.30s   best: 54.8915\n",
      "Epoch 727/1000:  train Loss: 51.7530   val Loss: 55.1964   time: 1.29s   best: 54.8915\n",
      "Epoch 728/1000:  train Loss: 51.4774   val Loss: 55.4742   time: 1.29s   best: 54.8915\n",
      "Epoch 729/1000:  train Loss: 51.7918   val Loss: 55.4566   time: 1.28s   best: 54.8915\n",
      "Epoch 730/1000:  train Loss: 51.5915   val Loss: 55.3613   time: 1.28s   best: 54.8915\n",
      "Epoch 731/1000:  train Loss: 51.1446   val Loss: 54.9566   time: 1.28s   best: 54.8915\n",
      "Epoch 732/1000:  train Loss: 51.0663   val Loss: 55.2104   time: 1.28s   best: 54.8915\n",
      "Epoch 733/1000:  train Loss: 51.2193   val Loss: 54.8514   time: 1.28s   best: 54.8514\n",
      "Epoch 734/1000:  train Loss: 51.2451   val Loss: 55.1943   time: 1.28s   best: 54.8514\n",
      "Epoch 735/1000:  train Loss: 51.1615   val Loss: 54.7648   time: 1.31s   best: 54.7648\n",
      "Epoch 736/1000:  train Loss: 50.8666   val Loss: 55.4579   time: 1.33s   best: 54.7648\n",
      "Epoch 737/1000:  train Loss: 51.3475   val Loss: 54.7909   time: 1.33s   best: 54.7648\n",
      "Epoch 738/1000:  train Loss: 50.9902   val Loss: 54.9037   time: 1.32s   best: 54.7648\n",
      "Epoch 739/1000:  train Loss: 51.1317   val Loss: 54.7546   time: 1.32s   best: 54.7546\n",
      "Epoch 740/1000:  train Loss: 51.0302   val Loss: 54.7350   time: 1.31s   best: 54.7350\n",
      "Epoch 741/1000:  train Loss: 50.9506   val Loss: 55.7136   time: 1.30s   best: 54.7350\n",
      "Epoch 742/1000:  train Loss: 51.5050   val Loss: 55.3510   time: 1.29s   best: 54.7350\n",
      "Epoch 743/1000:  train Loss: 52.1265   val Loss: 54.8168   time: 1.29s   best: 54.7350\n",
      "Epoch 744/1000:  train Loss: 50.8269   val Loss: 55.4628   time: 1.28s   best: 54.7350\n",
      "Epoch 745/1000:  train Loss: 51.1035   val Loss: 54.9702   time: 1.28s   best: 54.7350\n",
      "Epoch 746/1000:  train Loss: 50.8131   val Loss: 54.5174   time: 1.28s   best: 54.5174\n",
      "Epoch 747/1000:  train Loss: 50.8343   val Loss: 55.2751   time: 1.27s   best: 54.5174\n",
      "Epoch 748/1000:  train Loss: 50.6618   val Loss: 55.0177   time: 1.27s   best: 54.5174\n",
      "Epoch 749/1000:  train Loss: 50.9795   val Loss: 54.7308   time: 1.27s   best: 54.5174\n",
      "Epoch 750/1000:  train Loss: 50.7684   val Loss: 54.9671   time: 1.30s   best: 54.5174\n",
      "Epoch 751/1000:  train Loss: 50.9341   val Loss: 54.3536   time: 1.32s   best: 54.3536\n",
      "Epoch 752/1000:  train Loss: 51.1065   val Loss: 55.3387   time: 1.32s   best: 54.3536\n",
      "Epoch 753/1000:  train Loss: 51.1036   val Loss: 54.8558   time: 1.32s   best: 54.3536\n",
      "Epoch 754/1000:  train Loss: 50.5977   val Loss: 54.4670   time: 1.32s   best: 54.3536\n",
      "Epoch 755/1000:  train Loss: 50.8197   val Loss: 54.8238   time: 1.31s   best: 54.3536\n",
      "Epoch 756/1000:  train Loss: 50.6836   val Loss: 55.6490   time: 1.31s   best: 54.3536\n",
      "Epoch 757/1000:  train Loss: 50.7529   val Loss: 54.4817   time: 1.29s   best: 54.3536\n",
      "Epoch 758/1000:  train Loss: 50.5316   val Loss: 54.9474   time: 1.29s   best: 54.3536\n",
      "Epoch 759/1000:  train Loss: 50.7516   val Loss: 54.7291   time: 1.28s   best: 54.3536\n",
      "Epoch 760/1000:  train Loss: 51.0028   val Loss: 55.0443   time: 1.28s   best: 54.3536\n",
      "Epoch 761/1000:  train Loss: 50.5700   val Loss: 54.3938   time: 1.28s   best: 54.3536\n",
      "Epoch 762/1000:  train Loss: 50.4101   val Loss: 54.4246   time: 1.28s   best: 54.3536\n",
      "Epoch 763/1000:  train Loss: 50.8289   val Loss: 54.6912   time: 1.28s   best: 54.3536\n",
      "Epoch 764/1000:  train Loss: 50.7711   val Loss: 54.5862   time: 1.27s   best: 54.3536\n",
      "Epoch 765/1000:  train Loss: 50.8882   val Loss: 54.4751   time: 1.29s   best: 54.3536\n",
      "Epoch 766/1000:  train Loss: 50.5461   val Loss: 54.7856   time: 1.32s   best: 54.3536\n",
      "Epoch 767/1000:  train Loss: 50.6707   val Loss: 54.4946   time: 1.32s   best: 54.3536\n",
      "Epoch 768/1000:  train Loss: 50.4358   val Loss: 54.5759   time: 1.33s   best: 54.3536\n",
      "Epoch 769/1000:  train Loss: 50.1353   val Loss: 55.2907   time: 1.32s   best: 54.3536\n",
      "Epoch 770/1000:  train Loss: 50.3357   val Loss: 54.7689   time: 1.32s   best: 54.3536\n",
      "Epoch 771/1000:  train Loss: 50.2603   val Loss: 55.2636   time: 1.32s   best: 54.3536\n",
      "Epoch 772/1000:  train Loss: 50.5474   val Loss: 54.8259   time: 1.29s   best: 54.3536\n",
      "Epoch 773/1000:  train Loss: 50.4755   val Loss: 55.0758   time: 1.29s   best: 54.3536\n",
      "Epoch 774/1000:  train Loss: 50.4687   val Loss: 54.5224   time: 1.28s   best: 54.3536\n",
      "Epoch 775/1000:  train Loss: 50.4787   val Loss: 54.7164   time: 1.28s   best: 54.3536\n",
      "Epoch 776/1000:  train Loss: 50.2201   val Loss: 54.5913   time: 1.28s   best: 54.3536\n",
      "Epoch 777/1000:  train Loss: 50.3008   val Loss: 55.1642   time: 1.28s   best: 54.3536\n",
      "Epoch 778/1000:  train Loss: 50.6644   val Loss: 54.5470   time: 1.28s   best: 54.3536\n",
      "Epoch 779/1000:  train Loss: 50.1288   val Loss: 55.4803   time: 1.27s   best: 54.3536\n",
      "Epoch 780/1000:  train Loss: 50.1141   val Loss: 54.5385   time: 1.29s   best: 54.3536\n",
      "Epoch 781/1000:  train Loss: 50.1658   val Loss: 54.6819   time: 1.32s   best: 54.3536\n",
      "Epoch 782/1000:  train Loss: 50.0266   val Loss: 54.6824   time: 1.33s   best: 54.3536\n",
      "Epoch 783/1000:  train Loss: 50.1434   val Loss: 54.8937   time: 1.27s   best: 54.3536\n",
      "Epoch 784/1000:  train Loss: 50.2372   val Loss: 54.6288   time: 1.26s   best: 54.3536\n",
      "Epoch 785/1000:  train Loss: 50.4285   val Loss: 54.7267   time: 1.22s   best: 54.3536\n",
      "Epoch 786/1000:  train Loss: 50.1724   val Loss: 53.8262   time: 1.22s   best: 53.8262\n",
      "Epoch 787/1000:  train Loss: 49.7764   val Loss: 54.2473   time: 1.20s   best: 53.8262\n",
      "Epoch 788/1000:  train Loss: 49.9367   val Loss: 55.0531   time: 1.19s   best: 53.8262\n",
      "Epoch 789/1000:  train Loss: 49.6805   val Loss: 54.2739   time: 1.20s   best: 53.8262\n",
      "Epoch 790/1000:  train Loss: 49.9753   val Loss: 54.2638   time: 1.24s   best: 53.8262\n",
      "Epoch 791/1000:  train Loss: 50.0131   val Loss: 54.1421   time: 1.19s   best: 53.8262\n",
      "Epoch 792/1000:  train Loss: 49.7663   val Loss: 53.8409   time: 1.19s   best: 53.8262\n",
      "Epoch 793/1000:  train Loss: 49.7703   val Loss: 54.0061   time: 1.18s   best: 53.8262\n",
      "Epoch 794/1000:  train Loss: 49.6851   val Loss: 54.6290   time: 1.18s   best: 53.8262\n",
      "Epoch 795/1000:  train Loss: 49.8116   val Loss: 54.3277   time: 1.17s   best: 53.8262\n",
      "Epoch 796/1000:  train Loss: 49.6931   val Loss: 54.3345   time: 1.20s   best: 53.8262\n",
      "Epoch 797/1000:  train Loss: 49.8109   val Loss: 54.3840   time: 1.21s   best: 53.8262\n",
      "Epoch 798/1000:  train Loss: 50.0750   val Loss: 55.0664   time: 1.21s   best: 53.8262\n",
      "Epoch 799/1000:  train Loss: 50.1934   val Loss: 54.4250   time: 1.22s   best: 53.8262\n",
      "Epoch 800/1000:  train Loss: 49.7005   val Loss: 54.2411   time: 1.21s   best: 53.8262\n",
      "Epoch 801/1000:  train Loss: 49.5212   val Loss: 54.3757   time: 1.22s   best: 53.8262\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 802/1000:  train Loss: 49.5470   val Loss: 54.3522   time: 1.21s   best: 53.8262\n",
      "Epoch 803/1000:  train Loss: 49.7672   val Loss: 54.2296   time: 1.19s   best: 53.8262\n",
      "Epoch 804/1000:  train Loss: 49.4302   val Loss: 54.1365   time: 1.19s   best: 53.8262\n",
      "Epoch 805/1000:  train Loss: 49.2656   val Loss: 54.3237   time: 1.19s   best: 53.8262\n",
      "Epoch 806/1000:  train Loss: 49.5557   val Loss: 54.0589   time: 1.19s   best: 53.8262\n",
      "Epoch 807/1000:  train Loss: 49.5768   val Loss: 54.4196   time: 1.19s   best: 53.8262\n",
      "Epoch 808/1000:  train Loss: 49.7165   val Loss: 54.5831   time: 1.19s   best: 53.8262\n",
      "Epoch 809/1000:  train Loss: 49.3582   val Loss: 54.5339   time: 1.18s   best: 53.8262\n",
      "Epoch 810/1000:  train Loss: 49.5067   val Loss: 54.1360   time: 1.18s   best: 53.8262\n",
      "Epoch 811/1000:  train Loss: 49.2639   val Loss: 55.4339   time: 1.18s   best: 53.8262\n",
      "Epoch 812/1000:  train Loss: 49.6487   val Loss: 55.1329   time: 1.18s   best: 53.8262\n",
      "Epoch 813/1000:  train Loss: 49.9510   val Loss: 54.3451   time: 1.20s   best: 53.8262\n",
      "Epoch 814/1000:  train Loss: 49.5066   val Loss: 54.0627   time: 1.21s   best: 53.8262\n",
      "Epoch 815/1000:  train Loss: 49.5207   val Loss: 54.2324   time: 1.21s   best: 53.8262\n",
      "Epoch 816/1000:  train Loss: 49.2829   val Loss: 53.7080   time: 1.21s   best: 53.7080\n",
      "Epoch 817/1000:  train Loss: 49.4095   val Loss: 54.1582   time: 1.21s   best: 53.7080\n",
      "Epoch 818/1000:  train Loss: 49.2636   val Loss: 54.8854   time: 1.21s   best: 53.7080\n",
      "Epoch 819/1000:  train Loss: 49.6251   val Loss: 53.9979   time: 1.20s   best: 53.7080\n",
      "Epoch 820/1000:  train Loss: 49.1670   val Loss: 53.6189   time: 1.18s   best: 53.6189\n",
      "Epoch 821/1000:  train Loss: 49.2246   val Loss: 54.3030   time: 1.18s   best: 53.6189\n",
      "Epoch 822/1000:  train Loss: 49.3487   val Loss: 54.3980   time: 1.18s   best: 53.6189\n",
      "Epoch 823/1000:  train Loss: 49.1967   val Loss: 53.8993   time: 1.18s   best: 53.6189\n",
      "Epoch 824/1000:  train Loss: 49.0928   val Loss: 54.5836   time: 1.18s   best: 53.6189\n",
      "Epoch 825/1000:  train Loss: 49.5157   val Loss: 54.3122   time: 1.18s   best: 53.6189\n",
      "Epoch 826/1000:  train Loss: 49.6076   val Loss: 53.7276   time: 1.17s   best: 53.6189\n",
      "Epoch 827/1000:  train Loss: 48.9852   val Loss: 54.2939   time: 1.17s   best: 53.6189\n",
      "Epoch 828/1000:  train Loss: 49.1638   val Loss: 54.8584   time: 1.17s   best: 53.6189\n",
      "Epoch 829/1000:  train Loss: 49.1602   val Loss: 54.0029   time: 1.20s   best: 53.6189\n",
      "Epoch 830/1000:  train Loss: 48.7774   val Loss: 54.7139   time: 1.21s   best: 53.6189\n",
      "Epoch 831/1000:  train Loss: 49.2630   val Loss: 53.9729   time: 1.21s   best: 53.6189\n",
      "Epoch 832/1000:  train Loss: 49.3528   val Loss: 54.3885   time: 1.22s   best: 53.6189\n",
      "Epoch 833/1000:  train Loss: 48.9754   val Loss: 54.2471   time: 1.21s   best: 53.6189\n",
      "Epoch 834/1000:  train Loss: 48.9208   val Loss: 54.0183   time: 1.21s   best: 53.6189\n",
      "Epoch 835/1000:  train Loss: 49.1073   val Loss: 53.7794   time: 1.20s   best: 53.6189\n",
      "Epoch 836/1000:  train Loss: 49.1430   val Loss: 54.2609   time: 1.19s   best: 53.6189\n",
      "Epoch 837/1000:  train Loss: 48.8868   val Loss: 53.8214   time: 1.18s   best: 53.6189\n",
      "Epoch 838/1000:  train Loss: 48.8113   val Loss: 54.2164   time: 1.20s   best: 53.6189\n",
      "Epoch 839/1000:  train Loss: 48.9044   val Loss: 54.4172   time: 1.18s   best: 53.6189\n",
      "Epoch 840/1000:  train Loss: 48.9263   val Loss: 54.0680   time: 1.18s   best: 53.6189\n",
      "Epoch 841/1000:  train Loss: 48.7440   val Loss: 53.8905   time: 1.18s   best: 53.6189\n",
      "Epoch 842/1000:  train Loss: 48.8855   val Loss: 54.3480   time: 1.18s   best: 53.6189\n",
      "Epoch 843/1000:  train Loss: 49.1178   val Loss: 54.3258   time: 1.17s   best: 53.6189\n",
      "Epoch 844/1000:  train Loss: 48.9347   val Loss: 54.3488   time: 1.17s   best: 53.6189\n",
      "Epoch 845/1000:  train Loss: 48.7044   val Loss: 54.5303   time: 1.18s   best: 53.6189\n",
      "Epoch 846/1000:  train Loss: 49.4236   val Loss: 54.9624   time: 1.20s   best: 53.6189\n",
      "Epoch 847/1000:  train Loss: 49.4394   val Loss: 54.4092   time: 1.21s   best: 53.6189\n",
      "Epoch 848/1000:  train Loss: 48.5909   val Loss: 54.2954   time: 1.21s   best: 53.6189\n",
      "Epoch 849/1000:  train Loss: 48.5042   val Loss: 54.3856   time: 1.22s   best: 53.6189\n",
      "Epoch 850/1000:  train Loss: 48.7517   val Loss: 54.1554   time: 1.21s   best: 53.6189\n",
      "Epoch 851/1000:  train Loss: 48.6103   val Loss: 53.7890   time: 1.21s   best: 53.6189\n",
      "Epoch 852/1000:  train Loss: 48.8808   val Loss: 54.0794   time: 1.20s   best: 53.6189\n",
      "Epoch 853/1000:  train Loss: 48.6575   val Loss: 54.6253   time: 1.19s   best: 53.6189\n",
      "Epoch 854/1000:  train Loss: 48.4958   val Loss: 54.3289   time: 1.18s   best: 53.6189\n",
      "Epoch 855/1000:  train Loss: 48.8480   val Loss: 54.2500   time: 1.18s   best: 53.6189\n",
      "Epoch 856/1000:  train Loss: 48.8424   val Loss: 53.9446   time: 1.18s   best: 53.6189\n",
      "Epoch 857/1000:  train Loss: 48.5604   val Loss: 53.7351   time: 1.18s   best: 53.6189\n",
      "Epoch 858/1000:  train Loss: 49.4567   val Loss: 57.3776   time: 1.18s   best: 53.6189\n",
      "Epoch 859/1000:  train Loss: 49.5298   val Loss: 53.9962   time: 1.18s   best: 53.6189\n",
      "Epoch 860/1000:  train Loss: 48.2917   val Loss: 53.8883   time: 1.18s   best: 53.6189\n",
      "Epoch 861/1000:  train Loss: 48.3961   val Loss: 53.7220   time: 1.17s   best: 53.6189\n",
      "Epoch 862/1000:  train Loss: 48.5127   val Loss: 54.0046   time: 1.19s   best: 53.6189\n",
      "Epoch 863/1000:  train Loss: 48.2950   val Loss: 53.9508   time: 1.21s   best: 53.6189\n",
      "Epoch 864/1000:  train Loss: 48.2174   val Loss: 53.4854   time: 1.21s   best: 53.4854\n",
      "Epoch 865/1000:  train Loss: 48.6822   val Loss: 53.6482   time: 1.27s   best: 53.4854\n",
      "Epoch 866/1000:  train Loss: 48.2209   val Loss: 54.5769   time: 1.22s   best: 53.4854\n",
      "Epoch 867/1000:  train Loss: 48.2156   val Loss: 53.9759   time: 1.21s   best: 53.4854\n",
      "Epoch 868/1000:  train Loss: 48.1475   val Loss: 53.7455   time: 1.21s   best: 53.4854\n",
      "Epoch 869/1000:  train Loss: 48.2945   val Loss: 54.5465   time: 1.20s   best: 53.4854\n",
      "Epoch 870/1000:  train Loss: 48.0599   val Loss: 54.4236   time: 1.18s   best: 53.4854\n",
      "Epoch 871/1000:  train Loss: 48.2029   val Loss: 54.1042   time: 1.19s   best: 53.4854\n",
      "Epoch 872/1000:  train Loss: 48.3208   val Loss: 54.3609   time: 1.24s   best: 53.4854\n",
      "Epoch 873/1000:  train Loss: 48.5325   val Loss: 53.7703   time: 1.18s   best: 53.4854\n",
      "Epoch 874/1000:  train Loss: 47.6776   val Loss: 53.7949   time: 1.18s   best: 53.4854\n",
      "Epoch 875/1000:  train Loss: 48.4148   val Loss: 53.9154   time: 1.18s   best: 53.4854\n",
      "Epoch 876/1000:  train Loss: 48.0198   val Loss: 53.4336   time: 1.19s   best: 53.4336\n",
      "Epoch 877/1000:  train Loss: 47.9659   val Loss: 53.6435   time: 1.17s   best: 53.4336\n",
      "Epoch 878/1000:  train Loss: 48.0330   val Loss: 53.6367   time: 1.19s   best: 53.4336\n",
      "Epoch 879/1000:  train Loss: 48.0418   val Loss: 54.2041   time: 1.21s   best: 53.4336\n",
      "Epoch 880/1000:  train Loss: 48.2273   val Loss: 53.7053   time: 1.20s   best: 53.4336\n",
      "Epoch 881/1000:  train Loss: 47.8989   val Loss: 54.3488   time: 1.23s   best: 53.4336\n",
      "Epoch 882/1000:  train Loss: 48.0904   val Loss: 53.8425   time: 1.21s   best: 53.4336\n",
      "Epoch 883/1000:  train Loss: 47.8468   val Loss: 53.7019   time: 1.21s   best: 53.4336\n",
      "Epoch 884/1000:  train Loss: 48.1297   val Loss: 54.6568   time: 1.21s   best: 53.4336\n",
      "Epoch 885/1000:  train Loss: 48.1637   val Loss: 53.7143   time: 1.20s   best: 53.4336\n",
      "Epoch 886/1000:  train Loss: 48.2718   val Loss: 53.8042   time: 1.18s   best: 53.4336\n",
      "Epoch 887/1000:  train Loss: 48.2697   val Loss: 54.3244   time: 1.19s   best: 53.4336\n",
      "Epoch 888/1000:  train Loss: 48.1513   val Loss: 54.3453   time: 1.18s   best: 53.4336\n",
      "Epoch 889/1000:  train Loss: 48.1222   val Loss: 54.1451   time: 1.18s   best: 53.4336\n",
      "Epoch 890/1000:  train Loss: 47.9743   val Loss: 53.8893   time: 1.18s   best: 53.4336\n",
      "Epoch 891/1000:  train Loss: 47.5722   val Loss: 53.5751   time: 1.18s   best: 53.4336\n",
      "Epoch 892/1000:  train Loss: 47.7780   val Loss: 53.9457   time: 1.18s   best: 53.4336\n",
      "Epoch 893/1000:  train Loss: 48.1263   val Loss: 53.6608   time: 1.17s   best: 53.4336\n",
      "Epoch 894/1000:  train Loss: 48.9599   val Loss: 54.0292   time: 1.18s   best: 53.4336\n",
      "Epoch 895/1000:  train Loss: 48.3610   val Loss: 54.0546   time: 1.20s   best: 53.4336\n",
      "Epoch 896/1000:  train Loss: 47.8789   val Loss: 53.6852   time: 1.21s   best: 53.4336\n",
      "Epoch 897/1000:  train Loss: 47.7498   val Loss: 53.9901   time: 1.21s   best: 53.4336\n",
      "Epoch 898/1000:  train Loss: 47.9524   val Loss: 53.4105   time: 1.21s   best: 53.4105\n",
      "Epoch 899/1000:  train Loss: 47.4876   val Loss: 53.6565   time: 1.21s   best: 53.4105\n",
      "Epoch 900/1000:  train Loss: 49.5686   val Loss: 54.1415   time: 1.21s   best: 53.4105\n",
      "Epoch 901/1000:  train Loss: 49.0046   val Loss: 53.6067   time: 1.21s   best: 53.4105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 902/1000:  train Loss: 47.8639   val Loss: 53.5673   time: 1.19s   best: 53.4105\n",
      "Epoch 903/1000:  train Loss: 47.5713   val Loss: 53.4087   time: 1.19s   best: 53.4087\n",
      "Epoch 904/1000:  train Loss: 47.5861   val Loss: 53.2376   time: 1.18s   best: 53.2376\n",
      "Epoch 905/1000:  train Loss: 47.6207   val Loss: 53.6171   time: 1.18s   best: 53.2376\n",
      "Epoch 906/1000:  train Loss: 47.5790   val Loss: 53.7085   time: 1.18s   best: 53.2376\n",
      "Epoch 907/1000:  train Loss: 47.7745   val Loss: 53.2513   time: 1.18s   best: 53.2376\n",
      "Epoch 908/1000:  train Loss: 47.4497   val Loss: 53.5438   time: 1.18s   best: 53.2376\n",
      "Epoch 909/1000:  train Loss: 47.7627   val Loss: 53.5776   time: 1.17s   best: 53.2376\n",
      "Epoch 910/1000:  train Loss: 47.7235   val Loss: 53.5524   time: 1.17s   best: 53.2376\n",
      "Epoch 911/1000:  train Loss: 47.3788   val Loss: 53.3697   time: 1.19s   best: 53.2376\n",
      "Epoch 912/1000:  train Loss: 47.3247   val Loss: 53.2297   time: 1.21s   best: 53.2297\n",
      "Epoch 913/1000:  train Loss: 47.2201   val Loss: 53.3402   time: 1.21s   best: 53.2297\n",
      "Epoch 914/1000:  train Loss: 47.5906   val Loss: 53.2066   time: 1.22s   best: 53.2066\n",
      "Epoch 915/1000:  train Loss: 47.2591   val Loss: 53.8347   time: 1.22s   best: 53.2066\n",
      "Epoch 916/1000:  train Loss: 47.5207   val Loss: 53.2711   time: 1.21s   best: 53.2066\n",
      "Epoch 917/1000:  train Loss: 47.3423   val Loss: 53.2276   time: 1.21s   best: 53.2066\n",
      "Epoch 918/1000:  train Loss: 47.3309   val Loss: 53.6040   time: 1.20s   best: 53.2066\n",
      "Epoch 919/1000:  train Loss: 47.1840   val Loss: 53.3355   time: 1.19s   best: 53.2066\n",
      "Epoch 920/1000:  train Loss: 47.3739   val Loss: 53.5170   time: 1.18s   best: 53.2066\n",
      "Epoch 921/1000:  train Loss: 47.2286   val Loss: 53.1632   time: 1.18s   best: 53.1632\n",
      "Epoch 922/1000:  train Loss: 47.5136   val Loss: 53.8298   time: 1.18s   best: 53.1632\n",
      "Epoch 923/1000:  train Loss: 47.2517   val Loss: 53.5865   time: 1.18s   best: 53.1632\n",
      "Epoch 924/1000:  train Loss: 47.0365   val Loss: 53.9807   time: 1.18s   best: 53.1632\n",
      "Epoch 925/1000:  train Loss: 47.2459   val Loss: 53.5616   time: 1.18s   best: 53.1632\n",
      "Epoch 926/1000:  train Loss: 47.5093   val Loss: 53.9076   time: 1.17s   best: 53.1632\n",
      "Epoch 927/1000:  train Loss: 47.6577   val Loss: 54.4403   time: 1.18s   best: 53.1632\n",
      "Epoch 928/1000:  train Loss: 47.6276   val Loss: 53.1194   time: 1.20s   best: 53.1194\n",
      "Epoch 929/1000:  train Loss: 47.0239   val Loss: 53.8878   time: 1.22s   best: 53.1194\n",
      "Epoch 930/1000:  train Loss: 47.3648   val Loss: 53.6302   time: 1.21s   best: 53.1194\n",
      "Epoch 931/1000:  train Loss: 47.2101   val Loss: 53.3827   time: 1.22s   best: 53.1194\n",
      "Epoch 932/1000:  train Loss: 46.9894   val Loss: 53.3224   time: 1.21s   best: 53.1194\n",
      "Epoch 933/1000:  train Loss: 47.6252   val Loss: 54.0831   time: 1.21s   best: 53.1194\n",
      "Epoch 934/1000:  train Loss: 47.3237   val Loss: 54.4852   time: 1.20s   best: 53.1194\n",
      "Epoch 935/1000:  train Loss: 47.3083   val Loss: 53.2288   time: 1.18s   best: 53.1194\n",
      "Epoch 936/1000:  train Loss: 46.9784   val Loss: 53.7881   time: 1.18s   best: 53.1194\n",
      "Epoch 937/1000:  train Loss: 46.9856   val Loss: 53.2408   time: 1.18s   best: 53.1194\n",
      "Epoch 938/1000:  train Loss: 46.8943   val Loss: 53.5638   time: 1.18s   best: 53.1194\n",
      "Epoch 939/1000:  train Loss: 46.8862   val Loss: 53.2415   time: 1.18s   best: 53.1194\n",
      "Epoch 940/1000:  train Loss: 46.7848   val Loss: 53.4782   time: 1.18s   best: 53.1194\n",
      "Epoch 941/1000:  train Loss: 47.2307   val Loss: 53.4939   time: 1.18s   best: 53.1194\n",
      "Epoch 942/1000:  train Loss: 46.8081   val Loss: 53.0754   time: 1.17s   best: 53.0754\n",
      "Epoch 943/1000:  train Loss: 47.1755   val Loss: 52.8287   time: 1.18s   best: 52.8287\n",
      "Epoch 944/1000:  train Loss: 46.6734   val Loss: 53.4354   time: 1.20s   best: 52.8287\n",
      "Epoch 945/1000:  train Loss: 47.0238   val Loss: 53.5303   time: 1.21s   best: 52.8287\n",
      "Epoch 946/1000:  train Loss: 46.8074   val Loss: 53.2981   time: 1.21s   best: 52.8287\n",
      "Epoch 947/1000:  train Loss: 46.6231   val Loss: 53.7709   time: 1.27s   best: 52.8287\n",
      "Epoch 948/1000:  train Loss: 47.2939   val Loss: 53.6403   time: 1.27s   best: 52.8287\n",
      "Epoch 949/1000:  train Loss: 46.9527   val Loss: 53.6466   time: 1.22s   best: 52.8287\n",
      "Epoch 950/1000:  train Loss: 46.9552   val Loss: 53.4155   time: 1.21s   best: 52.8287\n",
      "Epoch 951/1000:  train Loss: 46.6125   val Loss: 53.8861   time: 1.19s   best: 52.8287\n",
      "Epoch 952/1000:  train Loss: 46.8276   val Loss: 52.9971   time: 1.18s   best: 52.8287\n",
      "Epoch 953/1000:  train Loss: 46.4575   val Loss: 53.0852   time: 1.26s   best: 52.8287\n",
      "Epoch 954/1000:  train Loss: 46.8253   val Loss: 53.4376   time: 1.29s   best: 52.8287\n",
      "Epoch 955/1000:  train Loss: 46.6105   val Loss: 52.7463   time: 1.28s   best: 52.7463\n",
      "Epoch 956/1000:  train Loss: 46.5312   val Loss: 53.8840   time: 1.27s   best: 52.7463\n",
      "Epoch 957/1000:  train Loss: 46.3231   val Loss: 53.1884   time: 1.27s   best: 52.7463\n",
      "Epoch 958/1000:  train Loss: 46.6259   val Loss: 53.5900   time: 1.27s   best: 52.7463\n",
      "Epoch 959/1000:  train Loss: 46.5051   val Loss: 53.2575   time: 1.27s   best: 52.7463\n",
      "Epoch 960/1000:  train Loss: 46.6934   val Loss: 53.2174   time: 1.30s   best: 52.7463\n",
      "Epoch 961/1000:  train Loss: 46.8413   val Loss: 53.4341   time: 1.31s   best: 52.7463\n",
      "Epoch 962/1000:  train Loss: 46.8136   val Loss: 53.2000   time: 1.31s   best: 52.7463\n",
      "Epoch 963/1000:  train Loss: 46.5155   val Loss: 53.1532   time: 1.31s   best: 52.7463\n",
      "Epoch 964/1000:  train Loss: 46.4166   val Loss: 53.1156   time: 1.31s   best: 52.7463\n",
      "Epoch 965/1000:  train Loss: 46.5509   val Loss: 53.7958   time: 1.31s   best: 52.7463\n",
      "Epoch 966/1000:  train Loss: 46.4545   val Loss: 53.0803   time: 1.30s   best: 52.7463\n",
      "Epoch 967/1000:  train Loss: 46.4459   val Loss: 53.5530   time: 1.28s   best: 52.7463\n",
      "Epoch 968/1000:  train Loss: 46.7842   val Loss: 53.2520   time: 1.28s   best: 52.7463\n",
      "Epoch 969/1000:  train Loss: 46.3779   val Loss: 53.3799   time: 1.28s   best: 52.7463\n",
      "Epoch 970/1000:  train Loss: 46.4712   val Loss: 53.5112   time: 1.28s   best: 52.7463\n",
      "Epoch 971/1000:  train Loss: 46.6295   val Loss: 53.7933   time: 1.28s   best: 52.7463\n",
      "Epoch 972/1000:  train Loss: 46.3403   val Loss: 53.5054   time: 1.27s   best: 52.7463\n",
      "Epoch 973/1000:  train Loss: 46.4306   val Loss: 54.2595   time: 1.27s   best: 52.7463\n",
      "Epoch 974/1000:  train Loss: 46.4053   val Loss: 53.6802   time: 1.27s   best: 52.7463\n",
      "Epoch 975/1000:  train Loss: 46.4095   val Loss: 53.2763   time: 1.29s   best: 52.7463\n",
      "Epoch 976/1000:  train Loss: 46.2093   val Loss: 53.4980   time: 1.32s   best: 52.7463\n",
      "Epoch 977/1000:  train Loss: 46.0603   val Loss: 53.5496   time: 1.31s   best: 52.7463\n",
      "Epoch 978/1000:  train Loss: 46.1027   val Loss: 53.5006   time: 1.32s   best: 52.7463\n",
      "Epoch 979/1000:  train Loss: 46.5325   val Loss: 53.9353   time: 1.31s   best: 52.7463\n",
      "Epoch 980/1000:  train Loss: 46.3326   val Loss: 54.1143   time: 1.31s   best: 52.7463\n",
      "Epoch 981/1000:  train Loss: 46.3070   val Loss: 53.4540   time: 1.31s   best: 52.7463\n",
      "Epoch 982/1000:  train Loss: 46.1480   val Loss: 53.4719   time: 1.29s   best: 52.7463\n",
      "Epoch 983/1000:  train Loss: 46.2703   val Loss: 53.2689   time: 1.28s   best: 52.7463\n",
      "Epoch 984/1000:  train Loss: 46.0785   val Loss: 53.0995   time: 1.28s   best: 52.7463\n",
      "Epoch 985/1000:  train Loss: 46.3464   val Loss: 53.5298   time: 1.28s   best: 52.7463\n",
      "Epoch 986/1000:  train Loss: 46.1897   val Loss: 53.1772   time: 1.27s   best: 52.7463\n",
      "Epoch 987/1000:  train Loss: 45.9543   val Loss: 52.9549   time: 1.27s   best: 52.7463\n",
      "Epoch 988/1000:  train Loss: 46.1632   val Loss: 53.1101   time: 1.27s   best: 52.7463\n",
      "Epoch 989/1000:  train Loss: 46.0685   val Loss: 53.1526   time: 1.27s   best: 52.7463\n",
      "Epoch 990/1000:  train Loss: 45.9614   val Loss: 52.9604   time: 1.27s   best: 52.7463\n",
      "Epoch 991/1000:  train Loss: 46.2558   val Loss: 54.0747   time: 1.29s   best: 52.7463\n",
      "Epoch 992/1000:  train Loss: 46.2297   val Loss: 52.9612   time: 1.33s   best: 52.7463\n",
      "Epoch 993/1000:  train Loss: 45.9364   val Loss: 53.1438   time: 1.32s   best: 52.7463\n",
      "Epoch 994/1000:  train Loss: 46.1402   val Loss: 52.9419   time: 1.31s   best: 52.7463\n",
      "Epoch 995/1000:  train Loss: 45.9551   val Loss: 53.1208   time: 1.31s   best: 52.7463\n",
      "Epoch 996/1000:  train Loss: 45.7890   val Loss: 52.8357   time: 1.31s   best: 52.7463\n",
      "Epoch 997/1000:  train Loss: 45.9149   val Loss: 53.3319   time: 1.31s   best: 52.7463\n",
      "Epoch 998/1000:  train Loss: 45.8380   val Loss: 52.9978   time: 1.29s   best: 52.7463\n",
      "Epoch 999/1000:  train Loss: 45.8405   val Loss: 53.7134   time: 1.28s   best: 52.7463\n",
      "Epoch 1000/1000:  train Loss: 46.0007   val Loss: 52.8611   time: 1.27s   best: 52.7463\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 20m 42s   best validation loss: 52.7463\n"
     ]
    }
   ],
   "source": [
    "%run utils_MultiLabel_vs_Classical.py\n",
    "input_size = 1272 \n",
    "\n",
    "# Definition\n",
    "model_Classical = None \n",
    "model_Classical = Model_Recursive_LSTM_v2(input_size,comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.275, 0.4, 0.275, 0.175, 0.175], output_size=106)\n",
    "model_Classical.to(train_device)\n",
    "\n",
    "criterion_106 = nn.CrossEntropyLoss()\n",
    "optimizer_106 = AdamW(model_Classical.parameters(),weight_decay=0.375e-2)  \n",
    "\n",
    "# Training\n",
    "\n",
    "bl_dict={'train':train_bl_106, 'val':val_bl_106}\n",
    "log_file_106 = 'log_106.txt'\n",
    "\n",
    "losses_106, best_model_106 = train_model_Classical(model_Classical, criterion_106, optimizer_106 , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=1000, logFile=log_file_106, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c648f74d",
   "metadata": {},
   "source": [
    "### Multi-Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "917f2f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000:  train Loss: 0.0158   val Loss: 0.0093   time: 1.26s   best: 0.0093\n",
      "Epoch 2/1000:  train Loss: 0.0132   val Loss: 0.0092   time: 1.28s   best: 0.0092\n",
      "Epoch 3/1000:  train Loss: 0.0124   val Loss: 0.0090   time: 1.27s   best: 0.0090\n",
      "Epoch 4/1000:  train Loss: 0.0119   val Loss: 0.0090   time: 1.22s   best: 0.0090\n",
      "Epoch 5/1000:  train Loss: 0.0116   val Loss: 0.0089   time: 1.27s   best: 0.0089\n",
      "Epoch 6/1000:  train Loss: 0.0113   val Loss: 0.0089   time: 1.23s   best: 0.0089\n",
      "Epoch 7/1000:  train Loss: 0.0110   val Loss: 0.0090   time: 1.17s   best: 0.0089\n",
      "Epoch 8/1000:  train Loss: 0.0108   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 9/1000:  train Loss: 0.0106   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 10/1000:  train Loss: 0.0105   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 11/1000:  train Loss: 0.0103   val Loss: 0.0088   time: 1.20s   best: 0.0088\n",
      "Epoch 12/1000:  train Loss: 0.0102   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 13/1000:  train Loss: 0.0101   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 14/1000:  train Loss: 0.0100   val Loss: 0.0088   time: 1.17s   best: 0.0088\n",
      "Epoch 15/1000:  train Loss: 0.0099   val Loss: 0.0087   time: 1.34s   best: 0.0087\n",
      "Epoch 16/1000:  train Loss: 0.0099   val Loss: 0.0088   time: 1.29s   best: 0.0087\n",
      "Epoch 17/1000:  train Loss: 0.0098   val Loss: 0.0088   time: 1.30s   best: 0.0087\n",
      "Epoch 18/1000:  train Loss: 0.0097   val Loss: 0.0087   time: 1.29s   best: 0.0087\n",
      "Epoch 19/1000:  train Loss: 0.0097   val Loss: 0.0087   time: 1.29s   best: 0.0087\n",
      "Epoch 20/1000:  train Loss: 0.0096   val Loss: 0.0087   time: 1.40s   best: 0.0087\n",
      "Epoch 21/1000:  train Loss: 0.0096   val Loss: 0.0087   time: 1.32s   best: 0.0087\n",
      "Epoch 22/1000:  train Loss: 0.0095   val Loss: 0.0087   time: 1.31s   best: 0.0087\n",
      "Epoch 23/1000:  train Loss: 0.0095   val Loss: 0.0087   time: 1.31s   best: 0.0087\n",
      "Epoch 24/1000:  train Loss: 0.0094   val Loss: 0.0087   time: 1.30s   best: 0.0087\n",
      "Epoch 25/1000:  train Loss: 0.0094   val Loss: 0.0087   time: 1.30s   best: 0.0087\n",
      "Epoch 26/1000:  train Loss: 0.0093   val Loss: 0.0087   time: 1.27s   best: 0.0087\n",
      "Epoch 27/1000:  train Loss: 0.0093   val Loss: 0.0087   time: 1.27s   best: 0.0087\n",
      "Epoch 28/1000:  train Loss: 0.0093   val Loss: 0.0087   time: 1.26s   best: 0.0087\n",
      "Epoch 29/1000:  train Loss: 0.0092   val Loss: 0.0087   time: 1.27s   best: 0.0087\n",
      "Epoch 30/1000:  train Loss: 0.0092   val Loss: 0.0086   time: 1.21s   best: 0.0086\n",
      "Epoch 31/1000:  train Loss: 0.0092   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 32/1000:  train Loss: 0.0092   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 33/1000:  train Loss: 0.0091   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 34/1000:  train Loss: 0.0091   val Loss: 0.0086   time: 1.21s   best: 0.0086\n",
      "Epoch 35/1000:  train Loss: 0.0091   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 36/1000:  train Loss: 0.0091   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 37/1000:  train Loss: 0.0091   val Loss: 0.0086   time: 1.35s   best: 0.0086\n",
      "Epoch 38/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.30s   best: 0.0086\n",
      "Epoch 39/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.29s   best: 0.0086\n",
      "Epoch 40/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 41/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 42/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 43/1000:  train Loss: 0.0090   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 44/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 45/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.29s   best: 0.0086\n",
      "Epoch 46/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 47/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 48/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.32s   best: 0.0086\n",
      "Epoch 49/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.33s   best: 0.0086\n",
      "Epoch 50/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 51/1000:  train Loss: 0.0089   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 52/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.24s   best: 0.0086\n",
      "Epoch 53/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 54/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 55/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 56/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.16s   best: 0.0086\n",
      "Epoch 57/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 58/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 59/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 60/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 61/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 62/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 63/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 64/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 65/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 66/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.22s   best: 0.0086\n",
      "Epoch 67/1000:  train Loss: 0.0088   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 68/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 69/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 70/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 71/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 72/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 73/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 74/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 75/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 76/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 77/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 78/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 79/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 80/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 81/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 82/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 83/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 84/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 85/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 86/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 87/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 88/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 89/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 90/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 91/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 92/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 93/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.24s   best: 0.0086\n",
      "Epoch 94/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 95/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 96/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 97/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.22s   best: 0.0086\n",
      "Epoch 98/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 99/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.24s   best: 0.0086\n",
      "Epoch 100/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.24s   best: 0.0086\n",
      "Epoch 101/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 103/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 104/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 105/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.25s   best: 0.0086\n",
      "Epoch 106/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 107/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 108/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 109/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 110/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 111/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 112/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 113/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 114/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 115/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.22s   best: 0.0086\n",
      "Epoch 116/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 117/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 118/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 119/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 120/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 121/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 122/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.27s   best: 0.0086\n",
      "Epoch 123/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.28s   best: 0.0086\n",
      "Epoch 124/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 125/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 126/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 127/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.29s   best: 0.0086\n",
      "Epoch 128/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 129/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 130/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 131/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 132/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 133/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 134/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 135/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.21s   best: 0.0086\n",
      "Epoch 136/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.23s   best: 0.0086\n",
      "Epoch 137/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 138/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 139/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 140/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 141/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 142/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 143/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 144/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 145/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 146/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 147/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 148/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 149/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 150/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 151/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 152/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 153/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 154/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 155/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 156/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.21s   best: 0.0086\n",
      "Epoch 157/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.26s   best: 0.0086\n",
      "Epoch 158/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 159/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 160/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.17s   best: 0.0086\n",
      "Epoch 161/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.18s   best: 0.0086\n",
      "Epoch 162/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.20s   best: 0.0086\n",
      "Epoch 163/1000:  train Loss: 0.0087   val Loss: 0.0086   time: 1.19s   best: 0.0086\n",
      "Epoch 164/1000:  train Loss: 0.0087   val Loss: 0.0085   time: 1.20s   best: 0.0085\n",
      "Epoch 165/1000:  train Loss: 0.0086   val Loss: 0.0084   time: 1.19s   best: 0.0084\n",
      "Epoch 166/1000:  train Loss: 0.0084   val Loss: 0.0083   time: 1.19s   best: 0.0083\n",
      "Epoch 167/1000:  train Loss: 0.0084   val Loss: 0.0082   time: 1.18s   best: 0.0082\n",
      "Epoch 168/1000:  train Loss: 0.0083   val Loss: 0.0082   time: 1.19s   best: 0.0082\n",
      "Epoch 169/1000:  train Loss: 0.0083   val Loss: 0.0082   time: 1.18s   best: 0.0082\n",
      "Epoch 170/1000:  train Loss: 0.0083   val Loss: 0.0082   time: 1.19s   best: 0.0082\n",
      "Epoch 171/1000:  train Loss: 0.0082   val Loss: 0.0082   time: 1.18s   best: 0.0082\n",
      "Epoch 172/1000:  train Loss: 0.0082   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 173/1000:  train Loss: 0.0082   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 174/1000:  train Loss: 0.0082   val Loss: 0.0082   time: 1.18s   best: 0.0081\n",
      "Epoch 175/1000:  train Loss: 0.0082   val Loss: 0.0082   time: 1.17s   best: 0.0081\n",
      "Epoch 176/1000:  train Loss: 0.0082   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 177/1000:  train Loss: 0.0082   val Loss: 0.0082   time: 1.19s   best: 0.0081\n",
      "Epoch 178/1000:  train Loss: 0.0082   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 179/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 180/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 181/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 182/1000:  train Loss: 0.0081   val Loss: 0.0082   time: 1.18s   best: 0.0081\n",
      "Epoch 183/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 184/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.19s   best: 0.0081\n",
      "Epoch 185/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.19s   best: 0.0081\n",
      "Epoch 186/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 187/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 188/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 189/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 190/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 191/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.21s   best: 0.0081\n",
      "Epoch 192/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.19s   best: 0.0081\n",
      "Epoch 193/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.18s   best: 0.0081\n",
      "Epoch 194/1000:  train Loss: 0.0081   val Loss: 0.0081   time: 1.19s   best: 0.0081\n",
      "Epoch 195/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.24s   best: 0.0081\n",
      "Epoch 196/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.26s   best: 0.0081\n",
      "Epoch 197/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.29s   best: 0.0081\n",
      "Epoch 198/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.28s   best: 0.0081\n",
      "Epoch 199/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.29s   best: 0.0080\n",
      "Epoch 200/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.32s   best: 0.0080\n",
      "Epoch 201/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.31s   best: 0.0080\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.30s   best: 0.0080\n",
      "Epoch 203/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.28s   best: 0.0080\n",
      "Epoch 204/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.26s   best: 0.0080\n",
      "Epoch 205/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 206/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 207/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.27s   best: 0.0080\n",
      "Epoch 208/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 209/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.24s   best: 0.0080\n",
      "Epoch 210/1000:  train Loss: 0.0080   val Loss: 0.0081   time: 1.19s   best: 0.0080\n",
      "Epoch 211/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.19s   best: 0.0080\n",
      "Epoch 212/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.19s   best: 0.0080\n",
      "Epoch 213/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.21s   best: 0.0080\n",
      "Epoch 214/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.18s   best: 0.0080\n",
      "Epoch 215/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.18s   best: 0.0080\n",
      "Epoch 216/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.17s   best: 0.0080\n",
      "Epoch 217/1000:  train Loss: 0.0080   val Loss: 0.0080   time: 1.17s   best: 0.0080\n",
      "Epoch 218/1000:  train Loss: 0.0079   val Loss: 0.0081   time: 1.22s   best: 0.0080\n",
      "Epoch 219/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.31s   best: 0.0080\n",
      "Epoch 220/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.25s   best: 0.0080\n",
      "Epoch 221/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.20s   best: 0.0080\n",
      "Epoch 222/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.18s   best: 0.0080\n",
      "Epoch 223/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.29s   best: 0.0080\n",
      "Epoch 224/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.29s   best: 0.0080\n",
      "Epoch 225/1000:  train Loss: 0.0079   val Loss: 0.0081   time: 1.38s   best: 0.0080\n",
      "Epoch 226/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.35s   best: 0.0080\n",
      "Epoch 227/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.32s   best: 0.0080\n",
      "Epoch 228/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.32s   best: 0.0080\n",
      "Epoch 229/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.32s   best: 0.0080\n",
      "Epoch 230/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.32s   best: 0.0080\n",
      "Epoch 231/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.33s   best: 0.0080\n",
      "Epoch 232/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.31s   best: 0.0080\n",
      "Epoch 233/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.33s   best: 0.0080\n",
      "Epoch 234/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.30s   best: 0.0080\n",
      "Epoch 235/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.25s   best: 0.0080\n",
      "Epoch 236/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.30s   best: 0.0080\n",
      "Epoch 237/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.29s   best: 0.0080\n",
      "Epoch 238/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 239/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.20s   best: 0.0080\n",
      "Epoch 240/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.19s   best: 0.0080\n",
      "Epoch 241/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.25s   best: 0.0080\n",
      "Epoch 242/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.28s   best: 0.0080\n",
      "Epoch 243/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 244/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.26s   best: 0.0080\n",
      "Epoch 245/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.26s   best: 0.0080\n",
      "Epoch 246/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.25s   best: 0.0080\n",
      "Epoch 247/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.27s   best: 0.0080\n",
      "Epoch 248/1000:  train Loss: 0.0079   val Loss: 0.0078   time: 1.26s   best: 0.0078\n",
      "Epoch 249/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.24s   best: 0.0078\n",
      "Epoch 250/1000:  train Loss: 0.0079   val Loss: 0.0077   time: 1.24s   best: 0.0077\n",
      "Epoch 251/1000:  train Loss: 0.0079   val Loss: 0.0077   time: 1.37s   best: 0.0077\n",
      "Epoch 252/1000:  train Loss: 0.0078   val Loss: 0.0080   time: 1.33s   best: 0.0077\n",
      "Epoch 253/1000:  train Loss: 0.0078   val Loss: 0.0080   time: 1.26s   best: 0.0077\n",
      "Epoch 254/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.22s   best: 0.0077\n",
      "Epoch 255/1000:  train Loss: 0.0079   val Loss: 0.0080   time: 1.17s   best: 0.0077\n",
      "Epoch 256/1000:  train Loss: 0.0078   val Loss: 0.0080   time: 1.29s   best: 0.0077\n",
      "Epoch 257/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.27s   best: 0.0077\n",
      "Epoch 258/1000:  train Loss: 0.0079   val Loss: 0.0077   time: 1.27s   best: 0.0077\n",
      "Epoch 259/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.27s   best: 0.0077\n",
      "Epoch 260/1000:  train Loss: 0.0079   val Loss: 0.0078   time: 1.30s   best: 0.0077\n",
      "Epoch 261/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.35s   best: 0.0077\n",
      "Epoch 262/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.30s   best: 0.0077\n",
      "Epoch 263/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 264/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 265/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0077\n",
      "Epoch 266/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 267/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 268/1000:  train Loss: 0.0078   val Loss: 0.0078   time: 1.29s   best: 0.0077\n",
      "Epoch 269/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.29s   best: 0.0077\n",
      "Epoch 270/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.35s   best: 0.0077\n",
      "Epoch 271/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.33s   best: 0.0077\n",
      "Epoch 272/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.31s   best: 0.0077\n",
      "Epoch 273/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.33s   best: 0.0077\n",
      "Epoch 274/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.32s   best: 0.0077\n",
      "Epoch 275/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.34s   best: 0.0077\n",
      "Epoch 276/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.33s   best: 0.0077\n",
      "Epoch 277/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.33s   best: 0.0077\n",
      "Epoch 278/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.31s   best: 0.0077\n",
      "Epoch 279/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.32s   best: 0.0077\n",
      "Epoch 280/1000:  train Loss: 0.0078   val Loss: 0.0078   time: 1.36s   best: 0.0077\n",
      "Epoch 281/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.37s   best: 0.0077\n",
      "Epoch 282/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.30s   best: 0.0077\n",
      "Epoch 283/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.24s   best: 0.0077\n",
      "Epoch 284/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 285/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0077\n",
      "Epoch 286/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0077\n",
      "Epoch 287/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0077\n",
      "Epoch 288/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0077\n",
      "Epoch 289/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.23s   best: 0.0077\n",
      "Epoch 290/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 291/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0077\n",
      "Epoch 292/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.26s   best: 0.0077\n",
      "Epoch 293/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.29s   best: 0.0077\n",
      "Epoch 294/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 295/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 296/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 297/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 298/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0077\n",
      "Epoch 299/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.34s   best: 0.0076\n",
      "Epoch 300/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.30s   best: 0.0076\n",
      "Epoch 301/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0076\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.31s   best: 0.0076\n",
      "Epoch 303/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.34s   best: 0.0076\n",
      "Epoch 304/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.33s   best: 0.0076\n",
      "Epoch 305/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.29s   best: 0.0076\n",
      "Epoch 306/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.28s   best: 0.0076\n",
      "Epoch 307/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.29s   best: 0.0076\n",
      "Epoch 308/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.32s   best: 0.0076\n",
      "Epoch 309/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 310/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.26s   best: 0.0076\n",
      "Epoch 311/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.22s   best: 0.0076\n",
      "Epoch 312/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.28s   best: 0.0076\n",
      "Epoch 313/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 314/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 315/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.18s   best: 0.0076\n",
      "Epoch 316/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 317/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.20s   best: 0.0076\n",
      "Epoch 318/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.21s   best: 0.0076\n",
      "Epoch 319/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 320/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 321/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.18s   best: 0.0076\n",
      "Epoch 322/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0076\n",
      "Epoch 323/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 324/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 325/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 326/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 327/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 328/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.18s   best: 0.0076\n",
      "Epoch 329/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 330/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.23s   best: 0.0076\n",
      "Epoch 331/1000:  train Loss: 0.0078   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 332/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 333/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 334/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 335/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 336/1000:  train Loss: 0.0078   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 337/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 338/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.23s   best: 0.0076\n",
      "Epoch 339/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 340/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 341/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 342/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 343/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.23s   best: 0.0076\n",
      "Epoch 344/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 345/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 346/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 347/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 348/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 349/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 350/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 351/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 352/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 353/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.21s   best: 0.0076\n",
      "Epoch 354/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.33s   best: 0.0076\n",
      "Epoch 355/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.34s   best: 0.0076\n",
      "Epoch 356/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.35s   best: 0.0076\n",
      "Epoch 357/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.34s   best: 0.0076\n",
      "Epoch 358/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 359/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 360/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 361/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.23s   best: 0.0076\n",
      "Epoch 362/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 363/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 364/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 365/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.19s   best: 0.0076\n",
      "Epoch 366/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 367/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 368/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.17s   best: 0.0076\n",
      "Epoch 369/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 370/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 371/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 372/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 373/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 374/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.22s   best: 0.0076\n",
      "Epoch 375/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 376/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.27s   best: 0.0076\n",
      "Epoch 377/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.27s   best: 0.0076\n",
      "Epoch 378/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 379/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 380/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 381/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 382/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 383/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.27s   best: 0.0076\n",
      "Epoch 384/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.37s   best: 0.0076\n",
      "Epoch 385/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.32s   best: 0.0076\n",
      "Epoch 386/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 387/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 388/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 389/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.17s   best: 0.0076\n",
      "Epoch 390/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 391/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 392/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 393/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 394/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 395/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 396/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 397/1000:  train Loss: 0.0077   val Loss: 0.0077   time: 1.18s   best: 0.0076\n",
      "Epoch 398/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 399/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.25s   best: 0.0076\n",
      "Epoch 400/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.21s   best: 0.0076\n",
      "Epoch 401/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.25s   best: 0.0076\n",
      "Epoch 403/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.24s   best: 0.0076\n",
      "Epoch 404/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.29s   best: 0.0076\n",
      "Epoch 405/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 406/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.33s   best: 0.0076\n",
      "Epoch 407/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 408/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 409/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.33s   best: 0.0076\n",
      "Epoch 410/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 411/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.29s   best: 0.0076\n",
      "Epoch 412/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.38s   best: 0.0076\n",
      "Epoch 413/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.31s   best: 0.0076\n",
      "Epoch 414/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 415/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.28s   best: 0.0076\n",
      "Epoch 416/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.28s   best: 0.0076\n",
      "Epoch 417/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 418/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.21s   best: 0.0076\n",
      "Epoch 419/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 420/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 421/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 422/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 423/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.23s   best: 0.0076\n",
      "Epoch 424/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.27s   best: 0.0076\n",
      "Epoch 425/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.26s   best: 0.0076\n",
      "Epoch 426/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.27s   best: 0.0076\n",
      "Epoch 427/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.24s   best: 0.0076\n",
      "Epoch 428/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.39s   best: 0.0076\n",
      "Epoch 429/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 430/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0076\n",
      "Epoch 431/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.19s   best: 0.0076\n",
      "Epoch 432/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.21s   best: 0.0076\n",
      "Epoch 433/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.20s   best: 0.0076\n",
      "Epoch 434/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.36s   best: 0.0076\n",
      "Epoch 435/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.34s   best: 0.0076\n",
      "Epoch 436/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.25s   best: 0.0076\n",
      "Epoch 437/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.34s   best: 0.0076\n",
      "Epoch 438/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.30s   best: 0.0076\n",
      "Epoch 439/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 440/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.22s   best: 0.0075\n",
      "Epoch 441/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 442/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.27s   best: 0.0075\n",
      "Epoch 443/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.21s   best: 0.0075\n",
      "Epoch 444/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 445/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 446/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 447/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 448/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.17s   best: 0.0075\n",
      "Epoch 449/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.17s   best: 0.0075\n",
      "Epoch 450/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 451/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 452/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 453/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 454/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 455/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 456/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 457/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 458/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.20s   best: 0.0075\n",
      "Epoch 459/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 460/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.26s   best: 0.0075\n",
      "Epoch 461/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 462/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.25s   best: 0.0075\n",
      "Epoch 463/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.21s   best: 0.0075\n",
      "Epoch 464/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 465/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.23s   best: 0.0075\n",
      "Epoch 466/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 467/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.26s   best: 0.0075\n",
      "Epoch 468/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.29s   best: 0.0075\n",
      "Epoch 469/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.36s   best: 0.0075\n",
      "Epoch 470/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.33s   best: 0.0075\n",
      "Epoch 471/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.30s   best: 0.0075\n",
      "Epoch 472/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.30s   best: 0.0075\n",
      "Epoch 473/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.30s   best: 0.0075\n",
      "Epoch 474/1000:  train Loss: 0.0077   val Loss: 0.0076   time: 1.30s   best: 0.0075\n",
      "Epoch 475/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 476/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.27s   best: 0.0075\n",
      "Epoch 477/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 478/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 479/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 480/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 481/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 482/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.33s   best: 0.0075\n",
      "Epoch 483/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 484/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 485/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.31s   best: 0.0075\n",
      "Epoch 486/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.34s   best: 0.0075\n",
      "Epoch 487/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.31s   best: 0.0075\n",
      "Epoch 488/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.37s   best: 0.0075\n",
      "Epoch 489/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.30s   best: 0.0075\n",
      "Epoch 490/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.33s   best: 0.0075\n",
      "Epoch 491/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.26s   best: 0.0075\n",
      "Epoch 492/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.21s   best: 0.0075\n",
      "Epoch 493/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 494/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 495/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 496/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 497/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 498/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 499/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 500/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 501/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.23s   best: 0.0075\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 502/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 503/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.32s   best: 0.0075\n",
      "Epoch 504/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.34s   best: 0.0075\n",
      "Epoch 505/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.31s   best: 0.0075\n",
      "Epoch 506/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 507/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 508/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.27s   best: 0.0075\n",
      "Epoch 509/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.26s   best: 0.0075\n",
      "Epoch 510/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.26s   best: 0.0075\n",
      "Epoch 511/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.28s   best: 0.0075\n",
      "Epoch 512/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.26s   best: 0.0075\n",
      "Epoch 513/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.26s   best: 0.0075\n",
      "Epoch 514/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.27s   best: 0.0075\n",
      "Epoch 515/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.27s   best: 0.0075\n",
      "Epoch 516/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.24s   best: 0.0075\n",
      "Epoch 517/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.18s   best: 0.0075\n",
      "Epoch 518/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.21s   best: 0.0075\n",
      "Epoch 519/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.23s   best: 0.0075\n",
      "Epoch 520/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 521/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.24s   best: 0.0075\n",
      "Epoch 522/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 523/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 524/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 525/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 526/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 527/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 528/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 529/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.24s   best: 0.0075\n",
      "Epoch 530/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 531/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 532/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 533/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 534/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 535/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 536/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 537/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 538/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 539/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 540/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 541/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 542/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 543/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 544/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.23s   best: 0.0075\n",
      "Epoch 545/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.22s   best: 0.0075\n",
      "Epoch 546/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 547/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 548/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.22s   best: 0.0075\n",
      "Epoch 549/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 550/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 551/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 552/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.22s   best: 0.0075\n",
      "Epoch 553/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 554/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 555/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 556/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.21s   best: 0.0075\n",
      "Epoch 557/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.27s   best: 0.0075\n",
      "Epoch 558/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.26s   best: 0.0075\n",
      "Epoch 559/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.27s   best: 0.0075\n",
      "Epoch 560/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.26s   best: 0.0075\n",
      "Epoch 561/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.29s   best: 0.0075\n",
      "Epoch 562/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 563/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 564/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 565/1000:  train Loss: 0.0076   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 566/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 567/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.27s   best: 0.0075\n",
      "Epoch 568/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.28s   best: 0.0075\n",
      "Epoch 569/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0075\n",
      "Epoch 570/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.22s   best: 0.0075\n",
      "Epoch 571/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 572/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 573/1000:  train Loss: 0.0076   val Loss: 0.0076   time: 1.19s   best: 0.0075\n",
      "Epoch 574/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.20s   best: 0.0075\n",
      "Epoch 575/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 576/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.17s   best: 0.0075\n",
      "Epoch 577/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 578/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.20s   best: 0.0075\n",
      "Epoch 579/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 580/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 581/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 582/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0075\n",
      "Epoch 583/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.22s   best: 0.0075\n",
      "Epoch 584/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 585/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0075\n",
      "Epoch 586/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.33s   best: 0.0075\n",
      "Epoch 587/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.34s   best: 0.0075\n",
      "Epoch 588/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.33s   best: 0.0075\n",
      "Epoch 589/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0075\n",
      "Epoch 590/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.36s   best: 0.0074\n",
      "Epoch 591/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.33s   best: 0.0074\n",
      "Epoch 592/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0074\n",
      "Epoch 593/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0074\n",
      "Epoch 594/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0074\n",
      "Epoch 595/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.29s   best: 0.0074\n",
      "Epoch 596/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 597/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 598/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.22s   best: 0.0074\n",
      "Epoch 599/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 600/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 601/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ydzey5IRwfWu8seG90rnjqyMOuiwrV67kzTffJBwOs2fPHmbNmkVcXBxvvvkmP/nJT3jmmWc+t8/y5ct5++23KS0t5YgjjuD666//3HXSH3/8MUuWLKFXr14cf/zxvPfee+Tn53Pttdcya9YsBgwYwNSpU1tdzs2bN3Prrbcyf/584uLiuPDCC3n++efp06cPmzZtYvFib/6d4uJiAH71q1+xdu1aEhMT65eJiMjhF/ipVOsyu72H0F188cWEw2EASkpKuPjiixk5ciS33HILS5YsaXGfr3zlKyQmJpKdnU1OTg7btm373DYTJ04kLy+PUCjE2LFjWbduHcuXL2fgwIH111QfTIDPnTuXgoICunfvTlxcHJdddhmzZs1i4MCBrFmzhu985zu89tpr9SP9R48ezWWXXcZf//rXfZ4aEBGRQ+9L9Rf4YFvKpaWlxCWl8Nm2Uvp1SyEjJaHdypKamlr/+mc/+xmTJ0/mueeeY926dfu8xCAxMbH+dTgcpra2tlXbfJEB/Pvat2vXrnzyySe8/vrr3H///Tz99NM89thjvPzyy8yaNYsZM2bwy1/+kiVLlijIRUQ6QOBb4HXnwCOH8CK2kpISevfuDcDjjz/e7scfNmwYa9asYd26dQD8/e8tzonToqOPPpp33nmHoqIiIpEITz31FCeddBJFRUVEo1EuvPBCfvnLX7JgwQKi0SgbN25k8uTJ/PrXv6a4uLhNE+iIiMgXF/imU91577ZeRtYaP/rRj7jyyiv5zW9+w8knn9zux09OTuYPf/gDU6ZMITs7m4kTJ+5z27feeou8vLz69//4xz+45557mDx5MpFIhLPPPptzzz2XTz75hG9+85tEY6P77rnnHiKRCJdffjklJSU457jlllvIzMxs9+8jIiIHZh00f0qb5Ofnu3nz5jVZtmzZMo488sg2Ha+0tJTUtDQWbyqhZ3oSOelJ7VHMDlFWVkZaWhrOOW688UaGDBnCLbfcclDHONwzsX2R/3aHg2aYaqC6aEr10UB10VR714eZzXfOtXi9ceC70ENmmBkRH/2QacnDDz/M2LFjGTFiBCUlJVx77bUdXSQRETmEAt+FDt50qlF/5ze33HLLQbe4RUTEvwLfAgcImxH1e4KLiEigKMDx5kM/lIPYRERE2psCHO88eEQtcBER8REFOBAO+X8Qm4iIBIsCnLpz4G3bt6CggNdff73Jst/97nfccMMN+92n7nK4s846q8U5xe+8807uvffe/X72888/z9KlS+vf//znP+fNN988iNK3TLcdFRHp/BTgQDhEm7vQp06dyvTp05ssmz59eqvnI3/llVfaPBlK8wD/xS9+wamnntqmY4mIiL8owGnoQm/LpDYXXXQRL730ElVVVQCsW7eOzZs3c8IJJ3D99deTn5/PiBEjuOOOO1rcv3///hQVFQFw9913c8QRR3DqqafW33IUvGu8jzrqKMaMGcOFF15IeXk577//PjNmzOCHP/whY8eOZfXq1UybNo1//vOfgDfj2rhx4xg1ahRXXXVVffn69+/PHXfcwfjx4xk1ahTLly9v9Xetu+3oyJEjddtREZEO9uW6DvzVH8PWRa3ePDlSC+E4ukWipNVGITEMNLulaM9RcOav9nmMrKwsJk6cyGuvvca5557L9OnTueSSSzAz7r77brp160YkEuGUU07h008/ZfTo0S0eZ/78+UyfPp2PP/6Y2tpaxo8fz4QJEwC44IILuPrqqwG4/fbbefTRR/nOd77DOeecw9lnn81FF13U5FiVlZVMmzaNt956i6FDh3LFFVfwwAMP8L3vfQ+A7OxsFixYwB/+8AfuvfdeHnnkkQPWVePbjnbt2pXTTz9dtx0VEelAaoHTENltHcbWuBu9cff5008/zfjx4xk3bhxLlixp0t3d3Lvvvsv5559PSkoK6enpnHPOOfXrFi9ezKRJkxg1ahRPPvnkPm9HWmfFihUMGDCAoUOHAnDllVcya9as+vUXXHABABMmTKi/AcqB6LajIiKdy5frL+t+WsotqYjN/b23vJoNu8oZ2qMLSfHhg/7Y8847j+9///ssWLCAiooKxo8fz9q1a7n33nuZO3cuXbt2Zdq0aVRWVu73OGbW4vJp06bx/PPPM2bMGB5//HFmzpy53+Mc6FRA3S1J93XL0oM5pm47KiLSMdQCxzsHDm0fyJaWlkZBQQFXXXVVfet7z549pKamkpGRwbZt23j11Vf3e4wTTzyR5557joqKCkpLS3nxxRfr15WWlpKbm0tNTQ1PPvlk/fIuXbpQWlr6uWMNGzaMdevWsWrVKgD+8pe/cNJJJ7Xpu9XRbUdFRDoXNYnwLiODtgc4eN3oF1xwQX1X+pgxYxg3bhwjRoxg4MCBHH/88fvdf/z48VxyySWMHTuWfv36MWnSpPp1v/zlLzn66KPp168fo0aNqg/tSy+9lKuvvpr77ruvfvAaQFJSEn/605+4+OKLqa2t5aijjuK66647qO+zv9uOOuc466yzdNtREZEOFPjbiXbp0oXKmggrt5XSt1sKmSkJ7VFUX9LtRJvSbRIbqC6aUn00UF00pduJHmZxsS70Wk2nKiIiPqEAp+EceG1EAS4iIv7wpQjwL3oawMyIC4Wobet8qnLQ/HTqRkSkM/J9gCclJbFz584vHAhxId2R7HBxzrFz506SkpI6uigiIr7l+1HoeXl5FBYWsmPHjoPet7Kysj5EdpR6U42Wb09s1/L5SeP6ONSSkpKajHIXEZGD4/sAj4+PZ8CAAW3ad+bMmYwbNw6A3z+5gGVb9/Dv/1fQjqXzl8b1ISIinZvvu9DbS7fUBHbtre7oYoiIiLSKAjymW2oCxeU11EY0kE1ERDo/BXhMdpo3gcvu8poOLomIiMiBKcBjuqV6g9d27q3q4JKIiIgcmAI8pluq1wLfVabz4CIi0vkpwGOyYl3oOzWQTUREfEABHpMVa4HvLFMXuoiIdH4K8JjMlATM0KVkIiLiCwrwmHDI6JqSoC50ERHxBQV4I1mazEVERHxCAd5Idloi20t1DlxERDo/BXgjuZlJbC2p7OhiiIiIHJACvJHcjCS27qnUbUVFRKTTU4A3kpuRTCTq6m8tKiIi0lkpwBvplendC3tLSUUHl0RERGT/FOCN9ExPBmCLzoOLiEgnpwBvpK4FvrlYLXAREencFOCNZCTHkxwf1kh0ERHp9BTgjZgZuRlJ6kIXEZFOTwHeTG5mEps1iE1ERDo5BXgzuRnJ6kIXEZFOTwHeTG5GEtv2VFIbiXZ0UURERPZJAd5MXtdkog42F6sVLiIinZcCvJlB3dMAWL2jrINLIiIism8K8GYG53gB/tn20g4uiYiIyL4pwJvJTEkgOy2BVdvVAhcRkc5LAd6CQd3TFOAiItKpKcBbMDjHC3DndFtRERHpnBTgLRick8aeylp2lOm2oiIi0jkpwFswJKcLgLrRRUSk01KAt6BuJPpqBbiIiHRSCvAW9EhPJC0xjs8U4CIi0kkpwFtgZgzK0Uh0ERHpvBTg+zBYl5KJiEgnpgDfh8E5aWwvraKkvKajiyIiIvI5rQpwM5tiZivMbJWZ/biF9WZm98XWf2pm4xute8zMtpvZ4mb7dDOzN8zss9hz1y/+ddrP6LwMABZs3N3BJREREfm8Awa4mYWB+4EzgeHAVDMb3myzM4Ehscc1wAON1j0OTGnh0D8G3nLODQHeir3vNMb37Up82JizZmdHF0VERORzWtMCnwiscs6tcc5VA9OBc5ttcy7whPPMATLNLBfAOTcL2NXCcc8F/hx7/WfgvDaU/5BJTggzJi+TD9e0VHQREZGO1ZoA7w1sbPS+MLbsYLdprodzbgtA7DmnFWU5rI4e2I1Fm0ooq6rt6KKIiIg0EdeKbayFZc0nCW/NNm1iZtfgdcvTo0cPZs6c2R6HBaCsrGy/x0surSUSdfxpxkxGdW9NVfnbgeojaFQfDVQXTak+Gqgumjqc9dGaVCoE+jR6nwdsbsM2zW0zs1zn3JZYd/v2ljZyzj0EPASQn5/vCgoKWlHk1pk5cyb7O95RVbXct/ANdibmUlAwot0+t7M6UH0EjeqjgeqiKdVHA9VFU4ezPlrThT4XGGJmA8wsAbgUmNFsmxnAFbHR6McAJXXd4/sxA7gy9vpK4IWDKPdhkZoYx6lH5vDiJ5upjUQ7ujgiIiL1Dhjgzrla4CbgdWAZ8LRzbomZXWdm18U2ewVYA6wCHgZuqNvfzJ4CPgCOMLNCM/tWbNWvgNPM7DPgtNj7TuesUbns3FvN4s17OrooIiIi9Vp1Ytc59wpeSDde9mCj1w64cR/7Tt3H8p3AKa0uaQcZ19e7PH3RphLG9sns2MKIiIjEaCa2A+iVkURWagKLCos7uigiIiL1FOAHYGaMysvg4w3FHV0UERGRegrwVjhmYBafbS9j257Kji6KiIgIoABvlRMGZwMw+7OiDi6JiIiIRwHeCsNz08lKTWD2KgW4iIh0DgrwVgiFjOMGZzN7VRHegHsREZGOpQBvpUlDstlRWsWiTSUdXRQREREFeGudMbwniXEhnp638cAbi4iIHGIK8FbKSInnrFG5vPDxZiqqIx1dHBERCTgF+EG45Kg+lFbV8uriA03zLiIicmgpwA/C0QO60T8rhelz1Y0uIiIdSwF+EMyMrx3Vh4/W7mLNjrKOLo6IiASYAvwgXTQ+j3DI+LsGs4mISAdSgB+knPQkTh6WwzPzC6nRPcJFRKSDKMDb4JL8PhSVVfPWsm0dXRQREQkoBXgbFBzRnX5ZKdz+/BKKyqo6ujgiIhJAwQ5w17bruePCIR64bAJFZVVM/2hDOxdKRETkwIIb4G/fw6R3vw5tnNt8eK90jhuUxZMfbqCyRhO7iIjI4RXcAE9KJxythMriNh/ippMHs6Wkkkdnr22/comIiLRCcAO8S0/vuXRrmw9x3KBszhjRg/vfXsX2PZXtVDAREZEDC3CA53rPpV9sWtSfnHUkNZEo//XainYolIiISOsEOMC/eAscoF9WKldPGsgzCwp5/uNN7VAwERGRAwtugKfVBfgXvzHJLacN5aj+Xbljhi4rExGRwyO4AZ6QQk1c6hdugQPEh0Pcc8Eoyqtr+Y+XluLaOLJdRESktYIb4EBVYjYUt8+c5oNzunBDwWCeX7iZP85a0y7HFBER2Ze4ji5ARypP6U1aUfsNPvvuKUNYtb2M/359BUlxIaYdP6Ddji0iItJYoFvg5Sl9Yfc6qKlol+OFQsYvzxvJkblduPPFpSzZXNIuxxUREWku0AG+N7UPuChsW9Jux+yWmsCT3z6GlIQwP35mEet37m23Y4uIiNQJdICXdhkEGDx9BURq2+24Gcnx/OZrY1i3cy9n/99sVu8oa7dji4iIQMADvDI5F467CfZsgpL2GcxWZ8rIXF7+ziTiwyHOv/895q/f1a7HFxGRYAt0gAMw9EzveccK2L6sXQ/dNyuFf153LBkp8Vz9xHx+8twiNhW3z/l2EREJNgV4t4He81OXwB+OgYridj38wO5p/H7qeCJRx98+3MB/vbq8XY8vIiLBpACvm1K1zo72n9N8TJ9M/v3/TuKS/D68+Olmbnv2Uwp3l7f754iISHAowM2gd37D+3a8LryxrLRE7jxnBBeNz+Of8ws57/73eG9VkWZtExGRNlGAA3z9afjRWggnHpIWeJ3khDD/ffEYXvveiaQkxHHZIx9y2SMf8sbSbUSjCnIREWm9QM/EVi81y3vOHgrrZsPu9bD8JRh+LmTktfvHDeqexqvfncRTH23gwXfWcPUT8xiSk0avzGQuP6Yfpw3v0e6fKSIiXy4K8MbGXwGv/hD+d7T3ft17MPVvh+SjUhPj+PakgUw7rj8vfbqFJz5YxzsrdzDrsx1cPCGPGwoG07dbCqGQHZLPFxERf1OAN5b/Tdi9Fub8wXu/aT7sXA2v3gq9x8Pkn7T7R8aFQ5w3rjfnjetN4e5y7n55GTM+2czT8wrJTktkSE4ayQlhrjlxIMcMzGr3zxcREX9SgDcWjocp98CEaTD/zzDnfvi/8d66de/CiT+C8KGrsryuKTxw+QS2llTywsJNzF5VRFlVLWuL9jL14TmM6p3BET26cOygLJLjw6Qnx3PcoCzM1EoXEQkaBXhLuh8Bp/8H5BwJb94B2UfAhvfhuWvgyHNgxHngnDeC/RDomZHEtScN4tqTBgGwt6qWP76zmnnrd/P6kq38Y35h/bYjeqXTt1sKw3qm0z87heT4MCkJceSkJ9IjPYn0pDjqBrqrO15E5MtDAb4voRCM/waMu9ybZvV3o2DxM7D4WVh8NpRthytfhEgNxCdDKHzIipKaGMf3Tz8CgEjUsWZHGbVRxwerd/LG0m0s31rKa0u20tIVaYlxIWoiUbLSEhmTl4lzjq6pCWQmx5OSGEdqQpiUxDhwjjWFNVQu3kpcyEhJDJMQDhFf/zBCIcOgvsVf93PADCz2rvFvmrrXZnX7edt5z94Bmrzfx7EByqq8ueqT4sNkpSao10FEAk8BfiBmkNEHzrgHuvaDv18Oy1701v39clj/AXTtD+f9AXJHH/LihEPGkB5dADgyN52rTvDuOV5eXcvm4koqayLsraplW2kV2/dUsr20iviw8dm2MjbsKsfMWLplD8XlNVTURD53/D8tnn/Iv8MXNWlINo9cmU9i3KH70SQi0tkpwFvDDI69wXs9dTr862eQ2Rc++xf0PRZ2rYFHToWRF0L2YEjNgf4nQG0VFK+HIacfsu72OikJcQzOSTuofaJRR0Us8AHemf0+R46ZQNQ5yqpqqY04aiJRaiKO6kgU51x9K9/hvXCORstiz87Vv8Z52zrnrXfN3tNo2/rjxF407lBIjg8TMmPJ5hL+/MF6Zq0s0uV2IhJoCvCDNfQM7xGNwrbF0GMkVOyGF2+GFS/DJyWf36d3vjdlaygOhk6Bqj1e8HfpCandvf3jU7xu+HD8YfsqoZCRmhhHaqL3z6B7SoiRvTMO2+e3RUV1L/4yZz2LNpUowEUk0BTgbRUKNXSZp2bBpU969xSv2uOdH18/22tS7loLmxdA0UqoKoOlzzc9TjgRIlXe64QukD0kdoMV551btxAkpkOXXC/cQ3HeIxwPoXhvVHxiF0jr6W3b5GGxR/PlsQfWaLsQ4dpyr4wt7YPR0Ca2hmO3Vn1TvQ3PdfvjSK7ey9DuySze1MIPJRGRAFGAt6dwHKR08x45wz6/PhqBVW96Le69O8DC3r3I03K890WfQfVe2DDHO1ZtlRdclcVQW3nIiz8JYPbB7lUX4odvKtgfZn2d2zZdeNg+T0SkM1KAH06hsNf9frAitVCz13uO1ngj36M1sfe1UL4TKnbFWrnR2KPx6yheizba7OGabLdq1WcMHjighX1iz3Ut7/r3jZZDyy3y+svtrIVn9rG88XOzbV6/jTEVH7K99GwqayIkxWsgm4gEkwLcD8JxED7056YLq2Yy+PiCQ/45X0htJdlv3UU2JWwqrmBQ94MbuCci8mWhu5GJvww4CYBjQkvZuEv3VBeR4FKAi7/kjiGa0IXjQkso3F3R0aUREekwCnDxl3Ac1v8Ejg2rBS4iwaYAF9+xXuPoZ9t4a9F6ItHDN/pdRKQzUYCL/2QPJoQjrngtH6ze2dGlERHpEApw8Z+sIQAMjdvKv5dv7+DCiIh0DAW4+E+Wd5vVk7rt5q3l24iqG11EAkgBLv6TkAqZ/TgmdQvrd5bz5EcbOrpEIiKHnQJc/Ckvn15lSxmTl8HPnl/MM/MLO7pEIiKHlQJc/CnvKGxPIU9fmseEfl35yXOL+NWry1m8qYRtew79vPEiIh1NAS7+NHAyWJjEx8/g0RP2cObInjz4zmrO/r/ZXPiH93A1muRFRL7cFODiTznD4NtvQEoWmc9cwu9238RfTihiRsJPmV15AXZ3T15/fx5b1y+Hjx6Gsh0dXWIRkXalm5mIf/WeAFe+CM9dA6veZNK2m3FxcRD1Vp/xr1PqNy1bN4+0M38Bq96AMVO9O8OJiPiYAlz8LTULLvsn/ONKSO6KHXMDPHIqVO0B4HU7njPce6QtnQ5Lp3v7fDIduvSE/Ktgz2aIVEPfY6DbQIhGYcMHkNmnA7+UiMiBtSrAzWwK8L9AGHjEOferZusttv4soByY5pxbsL99zWws8CCQBNQCNzjnPmqH7yRBYwZfe6Lh/W0bYetiCCdwWtYQ3nnjebq/fyeroz05KrSCnuve9bZb9I+mx+naH3avq3/bffgPoWwEpHX3FjgH696FvIkQn9R03/r7nouIHB4HDHAzCwP3A6cBhcBcM5vhnFvaaLMzgSGxx9HAA8DRB9j318BdzrlXzeys2PuCdvtmEmw9RwLeII+TzjifncefxerVO/ntyh289slaciLbeCPxRwAsTJxAv1ARXRuFN8CIpf8NhX+DE38A7/0v9BgJy1/yVh59HQwsgFA8vHQL9BgBF/wRyrZD0Ur48I8w4UoYeaEX7v/+pbd9vxO8oK+pgISUw1YdIvLl05oW+ERglXNuDYCZTQfOBRoH+LnAE845B8wxs0wzywX672dfB6TH9s8ANn/xryPSsqy0RL46phdfHdOLu84byTsrd3DH/D48ubSG2krvf4NsSpgU+pTTMzcRGXImaXsLOXHzHwm9dAskZTSEN8CHD3qPOiUb4Fd9m37o2negpBCiEXj3f7xH1/6Q2AW2LvLubX7KzyEvH3ashJ2fwZqZMOVXOkcvIgfUmgDvDWxs9L4Qr5V9oG16H2Df7wGvm9m9eA2l41pdapEvICk+zBkjenLGiLO4rSZCYlyIv8xZz6LCEorKBnP9ih3wIUAGvfkZJycuY0/2ZPYWbSJ/+GD65uVx+tCuxK17G968C46/GV7+AeCgzzHgIlBVBtFaeOPnTT+8cSt/7Tve+fojvwrLZjQs/+ghuPZd6D4M4hK8ZdEoFH4EPUd5M9GJSOCZ12jezwZmFwNnOOe+HXv/DWCic+47jbZ5GbjHOTc79v4t4EfAwH3ta2b3Ae84554xs68B1zjnTm3h868BrgHo0aPHhOnTp3/hL12nrKyMtLS0djue36k+wDlH1MHOSsc768qJi09g3rZaNpV9/v+TbknGeYPjSY03JnYpIqt0JdtzJjWcC3eOrJ0fAcaubuPpunshe9KHYa6WAWv/RlViN5Iqt9Nz69tY3dD5RioTs1g06g56bX6N7jveI6GmhFWDrqKwz7mHuBY+T/82mlJ9NFBdNNXe9TF58uT5zrn8lta1pgVeCDQekpvH57u797VNwn72vRL4buz1P4BHWvpw59xDwEMA+fn5rqCgoBVFbp2ZM2fSnsfzO9VHUzmx+ohGHbvKq6mojlAbdcxZs5Pbnl3ErkrHY4urAUhP6kqfbpO5pG8fFqzfTUJciOtOGkRe15PYXV7NyC6JmDX+fXpew8toBKpK4YUboeA2ePB4AJKqdnJU0T9g87v1mw7unsTgDvhvpH8bTak+Gqgumjqc9dGaAJ8LDDGzAcAm4FLg6822mQHcFDvHfTRQ4pzbYmY79rPvZuAkYCZwMvDZF/wuIodEKGRkpyXWvx+QncqpR/YgMyWeTwuL2b6niuc+3sR7q4r4+QtLSIoPUVkT5el5DfOz9+mWTHpSPKkJcdxy2lDeXLaNCf26ctaoXEqro3RJzoRLn/Q2Tu4KFbu916VbYdjZcMbd8OevQrnufy4ingMGuHOu1sxuAl7HuxTsMefcEjO7Lrb+QeAVvEvIVuFdRvbN/e0bO/TVwP+aWRxQSaybXMQPunfxAn1Cv24AnDkql4rqCKt3lDE4J41de6t5c9k2Nu4qJzk+zOxVRcSFQyxYv5upD88B4NHZa8lOS6CorJpbTh1Kj/REVm0v44ehFBLxAtyVF2FdCrzBbylZsLeoI76uiHRCrboO3Dn3Cl5IN172YKPXDrixtfvGls8GJhxMYUU6s+SEMCN7ZwDQKzOZK47tX7/u+6cfAcDyrXvYvqeKvt1SeOnTzazbWc4zCwr57Zsr67edllhFXuw0ulXs5l/rIzz6xw+4vTie9KIN3HDfuwzOSeNr+X0YnptOZko8pmvQRQJHM7GJHEbDeqYzrKf3+qaThwBw1zkjmLliB/2yUuifnQq/iYOqhn0+2BqiMifC+spkxkVXs2TPHpZs3sMLC73hJD3Tk9hbVUt+/670zEji7eU7GN8vk5OH9eBvH67n/HG9ueSovizaVMwnG0uYOKAbH2/YzRkjepKTntS8iCLiEwpwkQ6WmhjHV0bnNixICDUJ8J9feiI2/AR4/VXc3A9Z+KNTsVCIO2csIRwyNuwsJzkhzMrNxby9opqM5AReWbSVVxZtJTMlnp+9sISfvbDkc5/7mzdWUht1VNZECJnRNSWB88f3ZsH63USd4/avDGf9rnIykuPZUR4lGnWEQmrpi3QWCnCRzsY1vaTMUmNTuaZmY7WVZO5cCG//B7+9+M+Q0i22j4O7MtkwYio9pt7P8i2lrNhayvnje/Pv5dtZubWU3NAujh2Sy18Xl7OosITiimpG9c4kIzke5xxz1+3igZmrCRlEHZx7/3tNyvHIincxg6raKHEho1tqApOH5TC2TyZVtVFyM5JYW7SX1TvK2FZSyS2nDWVLSSWfbCwmPTmeKSN6UrS3irAZry/ZxoUTepMYF6ayJkJCOKQfByIHSQEu0tkMPR0WNJrbvT7AY8+Pne49f/p36DoAijd4N2cB+q5+Cp5Yz5ikdMZ85TcQDnHG8B6cMbwH3JUJczK49bYNTT9v88eAET3jWF5fuo2xfTMxjHUfPEuP6g1sHPYtXnt/IQuKIT4cIi0xjsraCIW7K/jVq8v3+TX+/MH6Ju9zM5LYUlJZ//4nzy2iV0YSRXurGdQ9jZtPHuz9mAA+21aKA/K6pjCiVzppSXGkJsTxaWExR+amkxSvmepEFOAinc1Z/wMTr4U/neW1xtN7ecuHnNF0u9d+3PL+G973nnuMhEEnw4vfhdzR3rKqkoYbr1SVwZ/PjgU4hNJ6cubXnoAMrzu/55zrARhQcAXJ2Zv4z2nTPvdRm4orWLZ5DymJYTbtrmBwThoDslP5aO0uZn22g24pCQzKSWPmih2s2FpKn24pbN9TyfGDsykur6E6EuWNpdtYtmUP1z+5oFXV0zszmcyUeDbuKmdA9zR27Knk+oJBfLyhmIkDupGdlsinhcUcMzCLTwpLGJCdSllVLUNy0hidl8GyLaWkJcaxYVc5vTKTGNg9jfdWFZGcEGZ8366tKsP+zFmzk+y0BAbndPnCxxLZHwW4SGcTl+DdjOXWdd60rOF4b3lad5j6dyjZ6IX6v26Hvsd6l5i9/Z94txcAjv8ebF4A797rPcCbZ73Ov26H3LGwfUl9eANQthWeOMe7zWrddegAvzmSowBSt0BqNky82lu+dAa9P3mK3pf8tWHu9ldvhYQ0Tj/lZ5ye8hnEJcFH93LuMVdBv0k453AOQoY31Ww4nmgkwntrdhGJOsIhY13RXgZ1TyM5Icw/5hcy/aMNRB2cPrwHOemJbCmupKImQu6ALEoqqinaW11/jv/ZjzfVF/u+f6/6XNVmpyVSVNYwwCAcMo4e0I33V3vX108c0I3huemEzJi5YjvHDMpiwfrddO+SSHJ8mFOOzOH8cXmsKYlwknNU1kT5pLCY5Pgwo/MyKC6v4dKH5pAQDvH1o/sye1URY/IyueeCUcSHjepIlLrJL+t6EdYW7WVvVW39FQwiraUAF+msQiG82wQ0csSUhtfDvtLw+ribAQcf3A/HXA9bPvWCfdDJ3kxvz3yrYdsPft/wOncMXPgY/H6Cd515fArM+UPL5XkndhfhJc+BhbxbqwL87RLYvRYy+8Hqt2LHHQ1PX9Gw76KnYfLt2M5V2Gm/gBdu8G70kpdPaNEzTDrrv2H8N6B8F5MSt3r3Zy/dxrgz+/Kf54/COYdVFsP2Zd5tYM/4T4hPBmD33mpeWLiJvlkplG9fx4gl95JecDMvbc1gVUmI3eXVXHJUH1ZvL2POml2M7pNB0Z5KJpTP4t/Vw5mzuZwbRkFFNMTc4lqeWVBIdW2Uqtooa4r2AjBo+xsMSSrm1qVT+Olzi6mNOt7a8SHb9lSxantZ/ddMo5wjrIgVkb48/v46AFZtL+ONpVvZU1nbsF1iHGP7ZPLN4/vzrT/PA+C/LhxFRnI8T320kYkDunHB+N6kJ8VTG3GUVNSwYVc5b6/YzqLCEk4b3oNvTxpAJOp4au5G8vt1JSstgZwuTa8qKCqrIhJ19Gh2tcE7K3ewqLC4/koI8KYRXrplD8N6phPWeARfOOBc6J1Jfn6+mzdvXrsdT1MANqX6aOpLUx/RCMx/HIac7rXMuw6A2kovDI84y2tVf/xXL+zTe3k3WVn9NmxZCJsXwuBT2b5tCzmDxsKc+71jJmVCZXHDZ2QNhp2fb/G2KC4ZaiuaLkvuCgU/8Y6/e533g+T9+6BLLoyZCoMmw/M3end9Axh+rveZmX3hsze8O7hl9vFu7TrvMW+bfifAN1/2bvH61l0wdIr3fec/Dp9M924Ok9nPuyNc3Q+c7yyArEEAVNVGWLR0CYO7xpP56DEAvHTyv3ivKJnl6zazO5KIRar5/qQcVlek8vTcjVxX/gBfj3ubr4buZ9CgoVx6VF/u/dcKVmwqoiIaRzp7OTNpEZ9mnkbh7gpKq7xQT6KKM0JzmRE9Dhf70WYGcSGjJtL0b/RQ28hKlwc0Ddm4kPGtEwZQHYmS1zWFjbvK+dtHG6iujfK1/DwM48Sh3YkPG9f8ZT4Ad58/kqxU75TDH2auBuBr+Xnc8dURLN5Uwu7yGk4a2p3khIYxB5U1Ea/M1cVUVpbzwaermDx5Ms45aqOO+HCjH50PFRAZUIA7+eeEQxaI+Qra+++Gme1zLnQF+JfhD3Q7UX00pfrAC/9QuKEu5j7qBdzAAi9oI7Ww4QMYcZ4XlBs/9O6HHpfs3Q/9f7wJbBh3OVTv9Vr569+Hs/4b9u6ArYthwIle131rJWZ45/Ib65LrnU5Y8mzT5SffDgv/BrvWeO9zRninDvbn6/+AjXO8e7z/81vUn5qoc9M83vl0HScVnAKPf8Ubc5AzHE69C/fizVjpFu80Rs5w6Nofl56L/W4U0cGnE1r1L+8Yk39KVWkRHzKaY5ffzepID4ZVfExxUh8qxlxBVdaRPLo0zOBQIctSj2FLSSVHD+zGKUkrOeK1qXzc82JeSr+EWVsT2LqnkpG9MohEHaM2/pUQUR6OnE1cyDFtSBX//Mwx1d7gxegxFLocAPraNvradmZHRwEQRy21hGn+owC8aYC7pSQQChm791azubiSxLgQH9qVpFDByYnT6Z3TjQ/X7iIrNYHjBmWzdnsJfRPL+N2mqQAcU/l/jO/XjWsndiNr4FjyuqYA8Ls3V5IUH+bSo/qQmZKw//8uPqEA3wcF+KGl+mhK9dGgzXXx1i+gpgKm3LP/7bYtgXWzYdda7xz87N9C1kD493/AhG96PxZGXQRjL4NIjdfN37U//ONKL7ir98K2xd6gv2Fney32hX+DTfMhJdvrkq+7n/vQKTD+SkhM8+aXr5PW0xsHsC+5Y71eiQNJyWo6Z33uGNjyyYH3a66ul2P0JTDuG7D4Ge9H0oqXG7bpNhCmvQxpPXHVZdivvHtHFR33c7Lf/wUAZUPPJ23lc0QSM7h70N+4oF8Fw9++hlDlLtYccQ3Vg05jyKtfJ0QUBp/C20fcwX0flnD26FzmrdvNeStvJTUhxP097uKIxF2MsrWcuPEBcmq8uf4n1DzCzkhKfZFGpZbwML+gZ2RLi19rVOUjpGV0Iz0xxK7tm9lBJgD9s1Lol5XKhm27OIrFJA47g+7pSSzZXMKxA7M4fnA2T320kQUbdnPZ0X1xQPKmD8jaNptN43/IlJE96ZLkjRdxzlETcSTENfQGfLR2F4/OXsOVx/XnuEHZ9ctfWLiJFVtL+dGUYQf/36gFCvB9UIAfWqqPplQfDTqsLopWeS3+fXW9Fn3mdYPHJXij62srIZzojR+I1MDWT72u9poKePYaOPEH0OdoiIvdnOblH8CO5d759Eufgrf/wzt9MP4K73P7neD1Ksz+DVz0J+9OccUb2JF9LN2LP/Z+DJz7e6+n4tmrvbKMvBD+NAV6jIJti/b93Xrnez8wsodA9lDvqoEN73s/AJY8d3D1FE6ESNWBt9uX1O7eD4W5j3qnN7IGQ86RuJJCrG6g44jzYekMb2BlI5t7nkJubi9vTENSOrb0hf1+1Nb00fw+5y7O3Pogx5e9zvW5T/POJsf4tF18pfZNssPlnFbxKt+u/n8kU8X4pM38V/lXiRKimnjyUqNs3gtH2gZeTvwJACdU/S87wj3o1y2ZxPg4dpZVUbS3mlOPzGH7niqiznHy5ofIoIx7469lal4R4e5DGDe4L2v/9j1Gh9bQ9cY3GdqjC9GoozoSZefealZuK2XyETn7/T7l1bUkx4f546w1nDGiJ+sXz1WAt0QBfmipPppSfTRQXcQUfQZ7NjFzAxQcPQYSukC4hbHAZdu9UNy22Bttn9YDyrZBOMEbAGghL+wjVZDUwujzJc/BwqfgnPtg5j3w8ZNw2i+gcK73I2T3ehh6BhTO8wYTrn7b630Yfk7Llxcee5M36c/s/4Uxl3jjH46+1rtqYM8m+NoT3riCDXO88QElhbBrNVQUQ9Ue73TA1k+9Fv8Fj0D3oV5vyR8nff6zRl3sXQYZnwQJaXDf2NgKo/50RGK6d9w63QZ5n7cfVfHpFA2+mF6rn6Y2FE985a76dc7imJt1DqN2vsaTiV9jW/JgKlLzmL3ZcUL6NhJcDT8v+RkAvw9dxk3RJ3krOoGnagt4JOF/AJgU9yT9c7uzZsde9lTWUBobdPizs4fTIz0Rw5i7ZjslFTVcftwgJvTryksfb6Dw2dtZ1/d8pq9O4PjQIs7sF+Lya3+03+9yMBTg+6A/Sk2pPppSfTRQXTR1WOsjGoXqMkhKb9326z+A9e955+GjtV6rvne+t3/dHAB1KnZ7Qdx7fMvHitR6QZvc1RukmN4bEhq6y1n0TxatWM2or97oXd5YvMH7YdHYyz+AuQ/Dz3d5Pw4+fAC2Lmp2asG8yyUj1d7bCx/1egM2vO+Nn0jMaBhACd7pjB0rPj8YsgmD5MyGSyIt7F1lUV3a4tZvJU+hoqqKj7POYllVDqeVv0L36o30ZzPJVLHc9eH40BLejE7gBzXXMSq8nl+GH2ZsaA3zokN5MzKe78Y9y2brwYDbFxKKa5+LvPYX4LqMTESkMwuFWh/eAP2O9R7g9Q4MOrlhXfNTEcldofd+Jq8JxzVM15s95PPrR13Ezp0zvfEEOUd6j+bO/DWc/h/eXAHjLvMeu9d7VzzUdc93G+j9uFjwOJTv8sY7jLjA6x3I9M7r02M4ZA2BvHzvWM55PRDZQ7wxElsWQvdh3iyGQ07zfgC4qNfTAXDqHV5vx/zHoaYcCudDxS7v9MqWhZxS8RoAZ+94O1ZXIVxWX3Yl9iEUn8Bpu5aSsHcvF4VncXqXdaSXN8xomB9aSX5oJbUJ6RSN+B6D2im8D0QBLiIih04oBKFmd73rGgvVPhObLj/xh033qwtv8FrijZlBn6O818ljoddY73Vf75I/RpzfcnmOub7pe+fgqaneaYER53s/Ckq3wphLsewhZDXeds078MQ5pFdu8X6UDDrZOw2x7l044z+JO+pqKma/3/LnHgIKcBERCS4z+Pr0hve9xu172/6T4LwHYNAp0KWHt+zs33oTKOV/yxtMeRgpwEVERFojFIKxX2+6LHsIfPV3HVOcDvlUERER+UIU4CIiIj6kABcREfEhBbiIiIgPKcBFRER8SAEuIiLiQwpwERERH1KAi4iI+JACXERExIcU4CIiIj6kABcREfEhBbiIiIgPKcBFRER8yJxzHV2GVjOzHcD6djxkNlDUjsfzO9VHU6qPBqqLplQfDVQXTbV3ffRzznVvaYWvAry9mdk851x+R5ejs1B9NKX6aKC6aEr10UB10dThrA91oYuIiPiQAlxERMSHgh7gD3V0AToZ1UdTqo8GqoumVB8NVBdNHbb6CPQ5cBEREb8KegtcRETElwIb4GY2xcxWmNkqM/txR5fnUDOzx8xsu5ktbrSsm5m9YWafxZ67Nlp3W6xuVpjZGR1T6kPHzPqY2dtmtszMlpjZd2PLA1cnZpZkZh+Z2SexurgrtjxwddGYmYXN7GMzeyn2PrD1YWbrzGyRmS00s3mxZYGsDzPLNLN/mtny2N+PYzusLpxzgXsAYWA1MBBIAD4Bhnd0uQ7xdz4RGA8sbrTs18CPY69/DPxX7PXwWJ0kAgNidRXu6O/QzvWRC4yPve4CrIx978DVCWBAWux1PPAhcEwQ66JZvXwf+BvwUux9YOsDWAdkN1sWyPoA/gx8O/Y6AcjsqLoIagt8IrDKObfGOVcNTAfO7eAyHVLOuVnArmaLz8X7x0js+bxGy6c756qcc2uBVXh19qXhnNvinFsQe10KLAN6E8A6cZ6y2Nv42MMRwLqoY2Z5wFeARxotDmx97EPg6sPM0vEaQ48COOeqnXPFdFBdBDXAewMbG70vjC0Lmh7OuS3gBRqQE1seqPoxs/7AOLyWZyDrJNZdvBDYDrzhnAtsXcT8DvgREG20LMj14YB/mdl8M7smtiyI9TEQ2AH8KXZ65REzS6WD6iKoAW4tLNNw/AaBqR8zSwOeAb7nnNuzv01bWPalqRPnXMQ5NxbIAyaa2cj9bP6lrgszOxvY7pyb39pdWlj2pamPmOOdc+OBM4EbzezE/Wz7Za6POLxTkQ8458YBe/G6zPflkNZFUAO8EOjT6H0esLmDytKRtplZLkDseXtseSDqx8zi8cL7Sefcs7HFga6TWHfgTGAKwa2L44FzzGwd3um1k83srwS3PnDObY49bweew+sGDmJ9FAKFsR4qgH/iBXqH1EVQA3wuMMTMBphZAnApMKODy9QRZgBXxl5fCbzQaPmlZpZoZgOAIcBHHVC+Q8bMDO881jLn3G8arQpcnZhZdzPLjL1OBk4FlhPAugBwzt3mnMtzzvXH+9vwb+fc5QS0Psws1cy61L0GTgcWE8D6cM5tBTaa2RGxRacAS+mouujoEX0d9QDOwht5vBr4aUeX5zB836eALUAN3q/CbwFZwFvAZ7Hnbo22/2msblYAZ3Z0+Q9BfZyA15X1KbAw9jgriHUCjAY+jtXFYuDnseWBq4sW6qaAhlHogawPvPO+n8QeS+r+Xga4PsYC82L/vzwPdO2outBMbCIiIj4U1C50ERERX1OAi4iI+JACXERExIcU4CIiIj6kABcREfEhBbiItAszK6i7c5eIHHoKcBERER9SgIsEjJldHrv/90Iz+2PsRiZlZvY/ZrbAzN4ys+6xbcea2Rwz+9TMnqu7z7GZDTazN2P3EF9gZoNih09rdK/kJ2Mz3onIIaAAFwkQMzsSuATv5hRjgQhwGZAKLHDeDSveAe6I7fIEcKtzbjSwqNHyJ4H7nXNjgOPwZvkD765u38O7D/JAvHnFReQQiOvoAojIYXUKMAGYG2scJ+PdeCEK/D22zV+BZ80sA8h0zr0TW/5n4B+xebF7O+eeA3DOVQLEjveRc64w9n4h0B+Yfci/lUgAKcBFgsWAPzvnbmuy0Oxnzbbb3xzL++sWr2r0OoL+xogcMupCFwmWt4CLzCwHwMy6mVk/vL8FF8W2+Tow2zlXAuw2s0mx5d8A3nHefdMLzey82DESzSzlcH4JEdGvY5FAcc4tNbPbgX+ZWQjv7nQ3AnuBEWY2HyjBO08O3q0RH4wF9Brgm7Hl3wD+aGa/iB3j4sP4NUQEdDcyEQEzK3POpXV0OUSk9dSFLiIi4kNqgYuIiPiQWuAiIiI+pAAXERHxIQW4iIiIDynARUREfEgBLiIi4kMKcBERER/6/9+3JfATss23AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 602/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 603/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 604/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 605/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 606/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 607/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.22s   best: 0.0074\n",
      "Epoch 608/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.34s   best: 0.0074\n",
      "Epoch 609/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 610/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.28s   best: 0.0074\n",
      "Epoch 611/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.30s   best: 0.0074\n",
      "Epoch 612/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.36s   best: 0.0074\n",
      "Epoch 613/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0074\n",
      "Epoch 614/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 615/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.31s   best: 0.0074\n",
      "Epoch 616/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.31s   best: 0.0074\n",
      "Epoch 617/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.31s   best: 0.0074\n",
      "Epoch 618/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.33s   best: 0.0074\n",
      "Epoch 619/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.35s   best: 0.0074\n",
      "Epoch 620/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.30s   best: 0.0074\n",
      "Epoch 621/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 622/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 623/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 624/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 625/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 626/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 627/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 628/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 629/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 630/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.24s   best: 0.0074\n",
      "Epoch 631/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 632/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.37s   best: 0.0074\n",
      "Epoch 633/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.34s   best: 0.0074\n",
      "Epoch 634/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.30s   best: 0.0074\n",
      "Epoch 635/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.32s   best: 0.0074\n",
      "Epoch 636/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.36s   best: 0.0074\n",
      "Epoch 637/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.29s   best: 0.0074\n",
      "Epoch 638/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.28s   best: 0.0074\n",
      "Epoch 639/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 640/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 641/1000:  train Loss: 0.0075   val Loss: 0.0076   time: 1.32s   best: 0.0074\n",
      "Epoch 642/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.33s   best: 0.0074\n",
      "Epoch 643/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 644/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 645/1000:  train Loss: 0.0075   val Loss: 0.0076   time: 1.29s   best: 0.0074\n",
      "Epoch 646/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.23s   best: 0.0074\n",
      "Epoch 647/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 648/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 649/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 650/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 651/1000:  train Loss: 0.0075   val Loss: 0.0076   time: 1.19s   best: 0.0074\n",
      "Epoch 652/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 653/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 654/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 655/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 656/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 657/1000:  train Loss: 0.0075   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 658/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 659/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 660/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 661/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 662/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 663/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 664/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.20s   best: 0.0074\n",
      "Epoch 665/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 666/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 667/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.26s   best: 0.0074\n",
      "Epoch 668/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.34s   best: 0.0074\n",
      "Epoch 669/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 670/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 671/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 672/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.35s   best: 0.0074\n",
      "Epoch 673/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 674/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 675/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 676/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.23s   best: 0.0074\n",
      "Epoch 677/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 678/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 679/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 680/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 681/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 682/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 683/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 684/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 685/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.19s   best: 0.0074\n",
      "Epoch 686/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 687/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 688/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 689/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 690/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 691/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 692/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 693/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 694/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 695/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 696/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 697/1000:  train Loss: 0.0075   val Loss: 0.0075   time: 1.24s   best: 0.0074\n",
      "Epoch 698/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.21s   best: 0.0074\n",
      "Epoch 699/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 700/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.17s   best: 0.0074\n",
      "Epoch 701/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.25s   best: 0.0074\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 702/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.28s   best: 0.0074\n",
      "Epoch 703/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 704/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 705/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 706/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.28s   best: 0.0074\n",
      "Epoch 707/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.29s   best: 0.0074\n",
      "Epoch 708/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 709/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 710/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 711/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 712/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 713/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 714/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 715/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.34s   best: 0.0074\n",
      "Epoch 716/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 717/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 718/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 719/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 720/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.24s   best: 0.0074\n",
      "Epoch 721/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 722/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 723/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 724/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 725/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 726/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.18s   best: 0.0074\n",
      "Epoch 727/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 728/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 729/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 730/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 731/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.26s   best: 0.0074\n",
      "Epoch 732/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.35s   best: 0.0074\n",
      "Epoch 733/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.28s   best: 0.0074\n",
      "Epoch 734/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 735/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.34s   best: 0.0074\n",
      "Epoch 736/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 737/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 738/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 739/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 740/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 741/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 742/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 743/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 744/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 745/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.42s   best: 0.0074\n",
      "Epoch 746/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 747/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 748/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 749/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.25s   best: 0.0074\n",
      "Epoch 750/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 751/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 752/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.22s   best: 0.0074\n",
      "Epoch 753/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.22s   best: 0.0074\n",
      "Epoch 754/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.29s   best: 0.0074\n",
      "Epoch 755/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.35s   best: 0.0074\n",
      "Epoch 756/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 757/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.33s   best: 0.0074\n",
      "Epoch 758/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 759/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 760/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.27s   best: 0.0074\n",
      "Epoch 761/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 762/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 763/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 764/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 765/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.29s   best: 0.0074\n",
      "Epoch 766/1000:  train Loss: 0.0073   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 767/1000:  train Loss: 0.0074   val Loss: 0.0075   time: 1.22s   best: 0.0074\n",
      "Epoch 768/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.22s   best: 0.0074\n",
      "Epoch 769/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.30s   best: 0.0074\n",
      "Epoch 770/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 771/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 772/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.32s   best: 0.0074\n",
      "Epoch 773/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.31s   best: 0.0074\n",
      "Epoch 774/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 775/1000:  train Loss: 0.0074   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 776/1000:  train Loss: 0.0073   val Loss: 0.0074   time: 1.18s   best: 0.0074\n",
      "Epoch 777/1000:  train Loss: 0.0073   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 778/1000:  train Loss: 0.0073   val Loss: 0.0074   time: 1.19s   best: 0.0074\n",
      "Epoch 779/1000:  train Loss: 0.0073   val Loss: 0.0074   time: 1.20s   best: 0.0074\n",
      "Epoch 780/1000:  train Loss: 0.0073   val Loss: 0.0073   time: 1.18s   best: 0.0073\n",
      "Epoch 781/1000:  train Loss: 0.0072   val Loss: 0.0071   time: 1.18s   best: 0.0071\n",
      "Epoch 782/1000:  train Loss: 0.0071   val Loss: 0.0070   time: 1.20s   best: 0.0070\n",
      "Epoch 783/1000:  train Loss: 0.0070   val Loss: 0.0070   time: 1.30s   best: 0.0070\n",
      "Epoch 784/1000:  train Loss: 0.0069   val Loss: 0.0069   time: 1.28s   best: 0.0069\n",
      "Epoch 785/1000:  train Loss: 0.0069   val Loss: 0.0070   time: 1.31s   best: 0.0069\n",
      "Epoch 786/1000:  train Loss: 0.0070   val Loss: 0.0069   time: 1.32s   best: 0.0069\n",
      "Epoch 787/1000:  train Loss: 0.0069   val Loss: 0.0071   time: 1.32s   best: 0.0069\n",
      "Epoch 788/1000:  train Loss: 0.0070   val Loss: 0.0069   time: 1.29s   best: 0.0069\n",
      "Epoch 789/1000:  train Loss: 0.0069   val Loss: 0.0069   time: 1.29s   best: 0.0069\n",
      "Epoch 790/1000:  train Loss: 0.0069   val Loss: 0.0069   time: 1.30s   best: 0.0069\n",
      "Epoch 791/1000:  train Loss: 0.0069   val Loss: 0.0069   time: 1.28s   best: 0.0069\n",
      "Epoch 792/1000:  train Loss: 0.0068   val Loss: 0.0069   time: 1.29s   best: 0.0069\n",
      "Epoch 793/1000:  train Loss: 0.0068   val Loss: 0.0069   time: 1.28s   best: 0.0069\n",
      "Epoch 794/1000:  train Loss: 0.0068   val Loss: 0.0069   time: 1.27s   best: 0.0069\n",
      "Epoch 795/1000:  train Loss: 0.0068   val Loss: 0.0069   time: 1.27s   best: 0.0069\n",
      "Epoch 796/1000:  train Loss: 0.0068   val Loss: 0.0070   time: 1.20s   best: 0.0069\n",
      "Epoch 797/1000:  train Loss: 0.0068   val Loss: 0.0069   time: 1.32s   best: 0.0069\n",
      "Epoch 798/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.33s   best: 0.0068\n",
      "Epoch 799/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.34s   best: 0.0068\n",
      "Epoch 800/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.33s   best: 0.0068\n",
      "Epoch 801/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.31s   best: 0.0068\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 802/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.28s   best: 0.0068\n",
      "Epoch 803/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.29s   best: 0.0068\n",
      "Epoch 804/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.27s   best: 0.0068\n",
      "Epoch 805/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 806/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.26s   best: 0.0068\n",
      "Epoch 807/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 808/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.26s   best: 0.0068\n",
      "Epoch 809/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.24s   best: 0.0068\n",
      "Epoch 810/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 811/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.26s   best: 0.0068\n",
      "Epoch 812/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 813/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.26s   best: 0.0068\n",
      "Epoch 814/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.26s   best: 0.0068\n",
      "Epoch 815/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 816/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.23s   best: 0.0068\n",
      "Epoch 817/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.18s   best: 0.0068\n",
      "Epoch 818/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.18s   best: 0.0068\n",
      "Epoch 819/1000:  train Loss: 0.0069   val Loss: 0.0068   time: 1.17s   best: 0.0068\n",
      "Epoch 820/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.22s   best: 0.0068\n",
      "Epoch 821/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 822/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.28s   best: 0.0068\n",
      "Epoch 823/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.28s   best: 0.0068\n",
      "Epoch 824/1000:  train Loss: 0.0067   val Loss: 0.0069   time: 1.26s   best: 0.0068\n",
      "Epoch 825/1000:  train Loss: 0.0068   val Loss: 0.0068   time: 1.21s   best: 0.0068\n",
      "Epoch 826/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.25s   best: 0.0068\n",
      "Epoch 827/1000:  train Loss: 0.0067   val Loss: 0.0069   time: 1.25s   best: 0.0068\n",
      "Epoch 828/1000:  train Loss: 0.0067   val Loss: 0.0069   time: 1.21s   best: 0.0068\n",
      "Epoch 829/1000:  train Loss: 0.0067   val Loss: 0.0068   time: 1.19s   best: 0.0068\n",
      "Epoch 830/1000:  train Loss: 0.0067   val Loss: 0.0067   time: 1.18s   best: 0.0067\n",
      "Epoch 831/1000:  train Loss: 0.0066   val Loss: 0.0066   time: 1.18s   best: 0.0066\n",
      "Epoch 832/1000:  train Loss: 0.0066   val Loss: 0.0066   time: 1.18s   best: 0.0066\n",
      "Epoch 833/1000:  train Loss: 0.0066   val Loss: 0.0065   time: 1.22s   best: 0.0065\n",
      "Epoch 834/1000:  train Loss: 0.0065   val Loss: 0.0065   time: 1.33s   best: 0.0065\n",
      "Epoch 835/1000:  train Loss: 0.0064   val Loss: 0.0065   time: 1.35s   best: 0.0065\n",
      "Epoch 836/1000:  train Loss: 0.0066   val Loss: 0.0067   time: 1.34s   best: 0.0065\n",
      "Epoch 837/1000:  train Loss: 0.0066   val Loss: 0.0068   time: 1.31s   best: 0.0065\n",
      "Epoch 838/1000:  train Loss: 0.0065   val Loss: 0.0064   time: 1.32s   best: 0.0064\n",
      "Epoch 839/1000:  train Loss: 0.0064   val Loss: 0.0064   time: 1.32s   best: 0.0064\n",
      "Epoch 840/1000:  train Loss: 0.0065   val Loss: 0.0065   time: 1.31s   best: 0.0064\n",
      "Epoch 841/1000:  train Loss: 0.0064   val Loss: 0.0064   time: 1.30s   best: 0.0064\n",
      "Epoch 842/1000:  train Loss: 0.0064   val Loss: 0.0064   time: 1.33s   best: 0.0064\n",
      "Epoch 843/1000:  train Loss: 0.0064   val Loss: 0.0063   time: 1.31s   best: 0.0063\n",
      "Epoch 844/1000:  train Loss: 0.0064   val Loss: 0.0064   time: 1.31s   best: 0.0063\n",
      "Epoch 845/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.27s   best: 0.0063\n",
      "Epoch 846/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.32s   best: 0.0063\n",
      "Epoch 847/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.32s   best: 0.0063\n",
      "Epoch 848/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.32s   best: 0.0063\n",
      "Epoch 849/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.28s   best: 0.0063\n",
      "Epoch 850/1000:  train Loss: 0.0063   val Loss: 0.0065   time: 1.21s   best: 0.0063\n",
      "Epoch 851/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 852/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.18s   best: 0.0063\n",
      "Epoch 853/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 854/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 855/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 856/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 857/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.19s   best: 0.0063\n",
      "Epoch 858/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 859/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.18s   best: 0.0063\n",
      "Epoch 860/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.18s   best: 0.0063\n",
      "Epoch 861/1000:  train Loss: 0.0062   val Loss: 0.0064   time: 1.18s   best: 0.0063\n",
      "Epoch 862/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.19s   best: 0.0063\n",
      "Epoch 863/1000:  train Loss: 0.0062   val Loss: 0.0065   time: 1.17s   best: 0.0063\n",
      "Epoch 864/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.20s   best: 0.0063\n",
      "Epoch 865/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.29s   best: 0.0063\n",
      "Epoch 866/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.31s   best: 0.0063\n",
      "Epoch 867/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.31s   best: 0.0063\n",
      "Epoch 868/1000:  train Loss: 0.0063   val Loss: 0.0066   time: 1.34s   best: 0.0063\n",
      "Epoch 869/1000:  train Loss: 0.0063   val Loss: 0.0063   time: 1.30s   best: 0.0063\n",
      "Epoch 870/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0063\n",
      "Epoch 871/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.29s   best: 0.0063\n",
      "Epoch 872/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.29s   best: 0.0063\n",
      "Epoch 873/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.29s   best: 0.0063\n",
      "Epoch 874/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.29s   best: 0.0063\n",
      "Epoch 875/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.28s   best: 0.0063\n",
      "Epoch 876/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.19s   best: 0.0062\n",
      "Epoch 877/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.24s   best: 0.0062\n",
      "Epoch 878/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.19s   best: 0.0062\n",
      "Epoch 879/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.26s   best: 0.0062\n",
      "Epoch 880/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.20s   best: 0.0062\n",
      "Epoch 881/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.23s   best: 0.0062\n",
      "Epoch 882/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.28s   best: 0.0062\n",
      "Epoch 883/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0062\n",
      "Epoch 884/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0062\n",
      "Epoch 885/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 886/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0062\n",
      "Epoch 887/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0062\n",
      "Epoch 888/1000:  train Loss: 0.0063   val Loss: 0.0065   time: 1.28s   best: 0.0062\n",
      "Epoch 889/1000:  train Loss: 0.0063   val Loss: 0.0064   time: 1.27s   best: 0.0062\n",
      "Epoch 890/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 891/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 892/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.30s   best: 0.0062\n",
      "Epoch 893/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 894/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.26s   best: 0.0062\n",
      "Epoch 895/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.25s   best: 0.0062\n",
      "Epoch 896/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.26s   best: 0.0062\n",
      "Epoch 897/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 898/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.25s   best: 0.0062\n",
      "Epoch 899/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.26s   best: 0.0062\n",
      "Epoch 900/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.27s   best: 0.0062\n",
      "Epoch 901/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.25s   best: 0.0062\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 902/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 903/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 904/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 905/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.24s   best: 0.0062\n",
      "Epoch 906/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 907/1000:  train Loss: 0.0061   val Loss: 0.0064   time: 1.25s   best: 0.0062\n",
      "Epoch 908/1000:  train Loss: 0.0062   val Loss: 0.0063   time: 1.25s   best: 0.0062\n",
      "Epoch 909/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.24s   best: 0.0062\n",
      "Epoch 910/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 911/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 912/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 913/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.28s   best: 0.0062\n",
      "Epoch 914/1000:  train Loss: 0.0062   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 915/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 916/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 917/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 918/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 919/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.31s   best: 0.0062\n",
      "Epoch 920/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.34s   best: 0.0062\n",
      "Epoch 921/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.31s   best: 0.0062\n",
      "Epoch 922/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.29s   best: 0.0062\n",
      "Epoch 923/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.32s   best: 0.0062\n",
      "Epoch 924/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.30s   best: 0.0062\n",
      "Epoch 925/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.32s   best: 0.0062\n",
      "Epoch 926/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 927/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.20s   best: 0.0062\n",
      "Epoch 928/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.19s   best: 0.0062\n",
      "Epoch 929/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.25s   best: 0.0062\n",
      "Epoch 930/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.21s   best: 0.0062\n",
      "Epoch 931/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.24s   best: 0.0062\n",
      "Epoch 932/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.19s   best: 0.0062\n",
      "Epoch 933/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.18s   best: 0.0062\n",
      "Epoch 934/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0062\n",
      "Epoch 935/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0062\n",
      "Epoch 936/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.33s   best: 0.0062\n",
      "Epoch 937/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.30s   best: 0.0062\n",
      "Epoch 938/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.29s   best: 0.0062\n",
      "Epoch 939/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.31s   best: 0.0061\n",
      "Epoch 940/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.29s   best: 0.0061\n",
      "Epoch 941/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.32s   best: 0.0061\n",
      "Epoch 942/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.35s   best: 0.0061\n",
      "Epoch 943/1000:  train Loss: 0.0061   val Loss: 0.0063   time: 1.37s   best: 0.0061\n",
      "Epoch 944/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.41s   best: 0.0061\n",
      "Epoch 945/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.34s   best: 0.0061\n",
      "Epoch 946/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0061\n",
      "Epoch 947/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0061\n",
      "Epoch 948/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.32s   best: 0.0061\n",
      "Epoch 949/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.31s   best: 0.0061\n",
      "Epoch 950/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.35s   best: 0.0061\n",
      "Epoch 951/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.35s   best: 0.0061\n",
      "Epoch 952/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.28s   best: 0.0061\n",
      "Epoch 953/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.40s   best: 0.0061\n",
      "Epoch 954/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.37s   best: 0.0061\n",
      "Epoch 955/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.30s   best: 0.0061\n",
      "Epoch 956/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.34s   best: 0.0061\n",
      "Epoch 957/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.26s   best: 0.0061\n",
      "Epoch 958/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0061\n",
      "Epoch 959/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.27s   best: 0.0061\n",
      "Epoch 960/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.28s   best: 0.0061\n",
      "Epoch 961/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.27s   best: 0.0061\n",
      "Epoch 962/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.27s   best: 0.0061\n",
      "Epoch 963/1000:  train Loss: 0.0060   val Loss: 0.0063   time: 1.25s   best: 0.0061\n",
      "Epoch 964/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.29s   best: 0.0061\n",
      "Epoch 965/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.30s   best: 0.0061\n",
      "Epoch 966/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.30s   best: 0.0061\n",
      "Epoch 967/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.21s   best: 0.0061\n",
      "Epoch 968/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.20s   best: 0.0061\n",
      "Epoch 969/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.26s   best: 0.0061\n",
      "Epoch 970/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.25s   best: 0.0061\n",
      "Epoch 971/1000:  train Loss: 0.0061   val Loss: 0.0061   time: 1.26s   best: 0.0061\n",
      "Epoch 972/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.26s   best: 0.0061\n",
      "Epoch 973/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.27s   best: 0.0061\n",
      "Epoch 974/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.26s   best: 0.0061\n",
      "Epoch 975/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.27s   best: 0.0061\n",
      "Epoch 976/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.27s   best: 0.0061\n",
      "Epoch 977/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.34s   best: 0.0061\n",
      "Epoch 978/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.28s   best: 0.0061\n",
      "Epoch 979/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.20s   best: 0.0061\n",
      "Epoch 980/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.22s   best: 0.0061\n",
      "Epoch 981/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.20s   best: 0.0061\n",
      "Epoch 982/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.18s   best: 0.0061\n",
      "Epoch 983/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.17s   best: 0.0061\n",
      "Epoch 984/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.18s   best: 0.0061\n",
      "Epoch 985/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.17s   best: 0.0061\n",
      "Epoch 986/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 987/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 988/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.22s   best: 0.0061\n",
      "Epoch 989/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 990/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.17s   best: 0.0061\n",
      "Epoch 991/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 992/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 993/1000:  train Loss: 0.0061   val Loss: 0.0062   time: 1.18s   best: 0.0061\n",
      "Epoch 994/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.19s   best: 0.0061\n",
      "Epoch 995/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 996/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 997/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.18s   best: 0.0061\n",
      "Epoch 998/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.23s   best: 0.0061\n",
      "Epoch 999/1000:  train Loss: 0.0060   val Loss: 0.0062   time: 1.33s   best: 0.0061\n",
      "Epoch 1000/1000:  train Loss: 0.0060   val Loss: 0.0061   time: 1.31s   best: 0.0061\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 20m 45s   best validation loss: 0.0061\n"
     ]
    }
   ],
   "source": [
    "%run utils_MultiLabel_vs_Classical.py\n",
    "input_size = 1272 \n",
    "\n",
    "# Definition\n",
    "model_MultiLabel = None \n",
    "model_MultiLabel = Model_Recursive_LSTM_v2(input_size,comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.275, 0.4, 0.275, 0.175, 0.175], output_size=15)\n",
    "model_MultiLabel.to(train_device)\n",
    "\n",
    "criterion_15 = nn.L1Loss()\n",
    "\n",
    "optimizer_15 = AdamW(model_MultiLabel.parameters(),weight_decay=0.375e-2)  \n",
    "\n",
    "# Training\n",
    "\n",
    "bl_dict={'train':train_bl_15, 'val':val_bl_15}\n",
    "log_file_15 = 'log_15.txt'\n",
    "\n",
    "losses_15, best_model_15 = train_model_multiLabel(model_MultiLabel, criterion_15, optimizer_15 , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=1000, logFile=log_file_15, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9907e8b2",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe9177ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(df):\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    return accuracy_score(df['target'].values.tolist(), df['prediction'].values.tolist())*100\n",
    "\n",
    "\n",
    "def hamming(df):\n",
    "    from sklearn.metrics import hamming_loss\n",
    "    return hamming_loss(df['target'].values.tolist(), df['prediction'].values.tolist())\n",
    "\n",
    "def Zero_One(df):\n",
    "    from sklearn.metrics import zero_one_loss\n",
    "    return zero_one_loss(df['target'].values.tolist(), df['prediction'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea4875bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 34/34 [00:00<00:00, 234.82it/s]\n",
      "100%|██████████| 22/22 [00:00<00:00, 269.94it/s]\n",
      "100%|██████████| 138/138 [00:00<00:00, 249.82it/s]\n"
     ]
    }
   ],
   "source": [
    "%run utils_MultiLabel_vs_Classical.py # imports and defines some utils functions\n",
    "\n",
    "# Classical\n",
    "val_df_106 = get_results_df_Classical(train_val_dataset_106, val_bl_106, val_indices_106, model_Classical)\n",
    "test_df_106 = get_results_df_Classical(test_dataset_106, test_bl_106, test_indices_106, model_Classical)\n",
    "train_df_106 = get_results_df_Classical(train_val_dataset_106, train_bl_106, train_indices_106, model_Classical)\n",
    "\n",
    "# Multi-Label\n",
    "val_df_15 = get_results_df_multiLabel(train_val_dataset_15, val_bl_15, val_indices_15, model_MultiLabel, threshhold = 0.5)\n",
    "test_df_15 = get_results_df_multiLabel(test_dataset_15, test_bl_15, test_indices_15, model_MultiLabel, threshhold = 0.5)\n",
    "train_df_15 = get_results_df_multiLabel(train_val_dataset_15, train_bl_15, train_indices_15, model_MultiLabel, threshhold = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e9a6185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical\n",
      "train 84.48055899036291 val 79.94182618607759 test 79.83193277310924\n",
      "Multi-Label\n",
      "train 69.58821566664703 val 65.65386580716167 test 68.40618600381329\n"
     ]
    }
   ],
   "source": [
    "# Classical\n",
    "print(\"Classical\")\n",
    "print( \"train\", accuracy(train_df_106), \"val\", accuracy(val_df_106), \"test\", accuracy(test_df_106) )\n",
    "\n",
    "# Multi-Label\n",
    "print(\"Multi-Label\")\n",
    "print( \"train\", accuracy(train_df_15), \"val\", accuracy(val_df_15), \"test\", accuracy(test_df_15) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6478b03f",
   "metadata": {},
   "source": [
    "## Evaluation on Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c8ded6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:00<00:00, 315.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:00<00:00, 327.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "benchmark_dataset_file ='/data/mm12191/datasets/benchmarks_ds2.json'\n",
    "# Classical\n",
    "bench_ds_106, bench_bl_106, bench_indices_106, _, _ = load_merge_data_classical(None, benchmark_dataset_file, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "# Multi-label\n",
    "bench_ds_15, bench_bl_15, bench_indices_15, _, _ = load_merge_data_multiLabel(None, benchmark_dataset_file, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cf5878d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 496.84it/s]\n"
     ]
    }
   ],
   "source": [
    "%run utils_MultiLabel_vs_Classical.py\n",
    "# Classical\n",
    "bench_df_106 = get_results_df_Classical(bench_ds_106, bench_bl_106, bench_indices_106, model_Classical)\n",
    "# Multi-Label\n",
    "bench_df_15 = get_results_df_multiLabel(bench_ds_15, bench_bl_15, bench_indices_15, model_MultiLabel, threshhold = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46cf88dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical\n",
      "bench 80.0\n",
      "Multi-Label\n",
      "bench 73.33333333333333\n"
     ]
    }
   ],
   "source": [
    "# Classical\n",
    "print(\"Classical\")\n",
    "print( \"bench\", accuracy(bench_df_106) )\n",
    "\n",
    "# Multi-Label\n",
    "print(\"Multi-Label\")\n",
    "print( \"bench\", accuracy(bench_df_15) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d61c8741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 0.040549045777803945 val 0.04579484559045111 test 0.04212508532824895 bench 0.035555555555555556\n",
      "train 0.30411784333352965 val 0.3434613419283833 test 0.31593813996186715 bench 0.2666666666666667\n"
     ]
    }
   ],
   "source": [
    "print( \"hamming score   : train\", hamming(train_df_15), \"val\", hamming(val_df_15), \"test\", hamming(test_df_15), \"bench\", hamming(bench_df_15) )\n",
    "print( \"Zero_One score   : train\", Zero_One(train_df_15), \"val\", Zero_One(val_df_15), \"test\", Zero_One(test_df_15), \"bench\", Zero_One(bench_df_15) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c1635aac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 478.31it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On benchmark\n",
    "%run utils_MultiLabel_vs_Classical.py\n",
    "bench_df_106 = get_results_df_Classical(bench_ds_106, bench_bl_106, bench_indices_106, model_Classical)\n",
    "Classical = []\n",
    "multiLabel = []\n",
    "I =[]\n",
    "for i in range(0,100,1):\n",
    "    bench_df_15 = get_results_df_multiLabel(bench_ds_15, bench_bl_15, bench_indices_15, model_MultiLabel, threshhold = i/100)\n",
    "    I.append(i/100)\n",
    "    multiLabel.append(accuracy(bench_df_15))\n",
    "    Classical.append(accuracy(bench_df_106))\n",
    "    \n",
    "\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(I,Classical, label='Classical Accuracy')\n",
    "plt.plot(I,multiLabel, label='Multi-label Accuracy')\n",
    "plt.title(\"Models' Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.xlabel('Threshhold')\n",
    "plt.legend(loc='lower right') # the plot evolves to the right\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49f1d75d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [00:00<00:00, 354.86it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QUwPUbPqFL21JBvvz2bFR1N1z200s/u156OaX0/x3uOVfirsqj/g/MGLswJS3AkPnIKzW0n6gGr+0JEld1G4L+84u9oguvinr0gBDKYALpRZwzdBp1EuSqmjiTBi3OzxUvWFx+2PoBHB7C7ibbgpJkiJg2idgyR/htWVZl2YoBXBLsft5iB6YIkmqgmn/CCRYOC/rkgyhAC40e9CKJGnzxu0O73wfLLy2b9cTqKKhE8BtLWBJkjZn2ifg5ceKpydlaOgEcNtVrSRJ2pzJxxXPTV54babFGDoB3GoXtCSpDCPHFa+I9fB1m86gycAQCuCCXdCSpPLUnwivvwTP3JlZEYZOABeavSyiJKk8ex5WHLzhkeszK8LQCeDWZlvAkqTyDN8Gdt4r0/OBh04AF5q9CpYkqXy1I4sjJ2Vk6ARwa8EuaElS+epGQvMbma1+CAWwXdCSpArUjbYFXBVeCUuSVAm7oKuktcV9wJKk8tWNsgu6KjwIS5JUibpRxfGBWwuZrH7oBHBri13QkqTy1Y0q3mbUDT2EAtiDsCRJFagdWbw1gPup0OJpSJKk8tWNLt5mtB946ARwq/uAJUkVqGtrAa/PZPVDKIAdD1iSVIH2fcC2gPun4EFYkqQK1HoQVnXYBS1JqkRbF3SzAdw/ngcsSaqEpyFViecBS5IqYRd0lXgpSklSJdpawJ6G1E8OxiBJqoQX4qiClLwSliSpMjU1mY6INDQCuO1C2nZBS5IqYQD3U2tL8dZLUUqSKpHhkIRDJICbi7d2QUuSKlE3yhZwvxRKAexBWJKkShjA/dTWBe0+YElSJdwH3E9tLWADWJJUibrR7gPul/aDsOyCliRVoG6kwxH2S3sXtAEsSapA7UiHI+yX9oOw7IKWJFXALuh+anUfsCSpD+pKB2GlNOirHiIBbBe0JKkP6kYBCZr/PuirHhoBXPAgLElSH2Q4JOHQCGC7oCVJfdE+JKEB3DeeByxJ6ou67IYkHBoB3OqlKCVJfVA3unibwalIQySA24YjNIAlSRWobWsBD/7FOIZGAHsesCSpL9r3AdsC7huHI5Qk9UWdR0H3T8HRkCRJfVDrQVj90z4YgwEsSaqAXdD9ZBe0JKkvtvQu6IjYPiKui4jHImJxRLw/IsZFxO0R8WTpdoeBLmyPCp6GJEnqg2G1MKxuyw1g4HvAbSmlvYB6YDFwLnBHSmlP4I7S42y0ug9YktRHtSO3zACOiLHAwcDPAFJKb6aU1gDHAFeWZrsSOHZgilgGr4QlSeqrjIYkLKcFvDvwMnB5RPwtIn4aEaOA8SmlFQCl210GsJyb1+pgDJKkPqobmcmFOCL1MgZiRDQA9wIHppTui4jvAWuBs1JK23eYb3VK6S37gSPiTOBMgPHjx+87d+7cqhV+/fr1jB49mnctncfuz17DXQdfR/JArIq11aP6x3qsDuuxOqzH8s2Y/y80127Hw9O++Zbn+luPhxxyyPyUUkN3z5XTZ7sMWJZSuq/0+DqK+3tXRsSElNKKiJgAvNTdi1NKlwKXAjQ0NKTGxsZKy9+jpqYmGhsb4c6/wLPwwcZZUDM0DuweTO31qH6xHqvDeqwO67ECz74NUmu39TWQ9dhrWqWUXgSej4j3lCbNAhYBNwGnlqadCtw4ICUsR2szRI3hK0mqXN2oTIYjLPeopbOAayKiDngGOI1ieM+LiM8CzwEnDEwRy9Da4jnAkqS+qcvmKOiyAjiltADorg97VlVL01eFFg/AkiT1Td0ohyPss9ZmT0GSJPVNbTZd0EMjgAvNtoAlSX2TURf00AhgW8CSpL6qG1U8lqjlzUFd7RAJ4IIHYUmS+qa2bUCGwb0Yx9AI4EKzQxFKkvomoyEJh0YAtzbbApYk9U1GQxIOjQAutLgPWJLUNwZwP7S22AUtSeqb2pHFWwO4D+yCliT1VV1psAX3AfeB5wFLkvqqrq0F7FHQlWt1H7AkqY/a9wHbAq5cwQtxSJL6qNbTkPqu1cEYJEl9ZBd0PzgcoSSpr4aPKI4pbxd0H3glLElSX0UUu6E9DakPHIxBktQfdYM/JOEQCWC7oCVJ/ZDBkIRDI4ALXglLktQPdaPcB9wnXglLktQftaM8CrpPvBKWJKk/6kZ5HnCfeCUsSVJ/uA+4jwxgSVJ/1I12H3Cf2AUtSeqP2pGehlSxlCAVPAhLktR3dV6Io3KF5uKtXdCSpL6qGwUtG6C1MGirzH8At7YUbz0PWJLUV7VtAzIMXit4CARwWwvYLmhJUh/VDf6QhPkP4EJbC9gAliT1UVsA2wKuQKv7gCVJ/WQA94EHYUmS+st9wH3Qahe0JKmf6kYXbwfxXOChE8AehCVJ6qs6W8CVa+uC9jQkSVJfte8D9ijo8nkQliSpv2rbTkOyBVw+u6AlSf3lUdB9UPBKWJKkfmo/Ctou6PJ5JSxJUn/V1MDwbeHN9YO3ykFb00BpPwjLAJYk9UPdKC9FWZH2fcB2QUuS+qFupPuAK2IAS5KqoW60AVwRu6AlSdVQawu4Mh6EJUmqBvcBV6hgF7QkqQrqRg1qCzj/qdXqecCSpCr4yIVAGrTV5T+17IKWJFXDDrsO6uqGQBe0B2FJkvIn/wHsaUiSpBzKfwAXHA1JkpQ/+Q/g9oOw7IKWJOXH0AlgD8KSJOVI/gO4vQt6WLblkCSpAvkP4NbmYus3IuuSSJJUtiEQwC0egCVJyp38B3ChxQOwJEm5k/8Abm22BSxJyp38B3Ch2RawJCl38h/A7gOWJOWQASxJUgbKSq6IWAKsAwpAS0qpISLGAdcCk4AlwD+mlFYPTDE3wy5oSVIOVdICPiSlND2l1FB6fC5wR0ppT+CO0uPB13YesCRJOdKfLuhjgCtL968Eju13afqi0ALD7IKWJOVLuQGcgN9FxPyIOLM0bXxKaQVA6XaXgShgr9wHLEnKoXKT68CU0gsRsQtwe0Q8Vu4KSoF9JsD48eNpamqqvJQ9WL9+Pa+ueolhhQ38rYrL3dqsX7++qu/L1sp6rA7rsTqsx+oYyHosK4BTSi+Ubl+KiBuA/YCVETEhpbQiIiYAL/Xw2kuBSwEaGhpSY2NjVQoO0NTUxLixY4DRVHO5W5umpibrrwqsx+qwHqvDeqyOgazHXrugI2JURIxpuw8cBjwC3AScWprtVODGASlhb7wSliQph8pJrvHADVEcbWg48IuU0m0RcT8wLyI+CzwHnDBwxdyMQjPUjc5k1ZIk9VWvAZxSegao72b6KmDWQBSqIq0OxiBJyh+vhCVJUgbyH8BeCUuSlEP5D2CvhCVJyqEhEMAFu6AlSbmT/wAuNHspSklS7uQ/gO2CliTlUP4DuOBpSJKk/Ml/AHslLElSDg2BAPY8YElS/uQ/gD0PWJKUQ/kO4FQAkgdhSZJyJ9cBXNNaKN7xNCRJUs7kOoAjlQLYfcCSpJwZIgFsF7QkKV9yHsAtxTsehCVJypmcB7Bd0JKkfMp1ANe0llrABrAkKWdyHcCRWot37IKWJOVMzgPYFrAkKZ9yHsBt5wHbApYk5UvOA7itBWwAS5LyJecB7FHQkqR8ynUAeylKSVJe5TqA7YKWJOVVzgPYg7AkSfk0NALYfcCSpJwxgCVJykCuA7j9UpR2QUuScibXAexwhJKkvMp5ALe1gO2CliTlS84DuDQYg/uAJUk5k/MA9jxgSVI+5TqAN10JywCWJOVLrgPY4QglSXmV8wC2BSxJyqehEcC2gCVJOTNEAtgWsCQpX3IdwDWtLRA1UJPrzZAkbYVynVyRCrZ+JUm5NAQC2P2/kqT8yXkAt3gZSklSLuU8gO2CliTlU64DuKa1xXOAJUm5lOsAtgUsScqrIRDAw7IuhiRJFct/ANsFLUnKoZwHcItd0JKkXMp1ANe0FjwNSZKUS7kOYC/EIUnKqyEQwHZBS5LyJ+cB7HnAkqR8ynkA2wUtScqnXAewV8KSJOVVrgM4UqstYElSLuU8gFsMYElSLuU8gL0SliQpn3IewF4JS5KUTzkPYI+CliTlU9kBHBHDIuJvEXFz6fG4iLg9Ip4s3e4wcMXsnpeilCTlVSUt4HOAxR0enwvckVLaE7ij9HhQeSUsSVJelRXAETER+Afgpx0mHwNcWbp/JXBsVUtWBq+EJUnKq3JbwN8Fvgq0dpg2PqW0AqB0u0t1i9Y79wFLkvKq1/SKiI8CL6WU5kdEY6UriIgzgTMBxo8fT1NTU6WL6NHBqcDSZS/wbBWXuTVav359Vd+XrZX1WB3WY3VYj9UxkPVYTvPxQODoiDgSGAGMjYifAysjYkJKaUVETABe6u7FKaVLgUsBGhoaUmNjY3VKnhI0Fdh1tz3YtVrL3Eo1NTVRtfdlK2Y9Vof1WB3WY3UMZD322gWdUvpaSmliSmkScCLwh5TSp4GbgFNLs50K3DggJexJa0vx1oOwJEk51J/zgC8EDo2IJ4FDS48HT6G5eOtpSJKkHKoovVJKTUBT6f4qYFb1i1Sm9hawASxJyp/8XgnLLmhJUo7lt/loF7SkQdTc3MyyZcvYsGFD1kUpy3bbbcfixYt7n1GbVW49jhgxgokTJ1JbW36jML/p1VoKYFvAkgbBsmXLGDNmDJMmTSIisi5Or9atW8eYMWOyLkbulVOPKSVWrVrFsmXL2G233cpedn67oNtbwAawpIG3YcMGdtxxx1yErwZXRLDjjjtW3DuS3wBuLRRvPQhL0iAxfNWTvnw2chzAbV3QBrCkrcOLL77IiSeeyB577MF73/tejjzySJ544gmWLFnClClTqraeb37zm/z+97+v+HW9lePiiy9mxIgRvPbaa/0p3pCR3wC2C1rSViSlxHHHHUdjYyNPP/00ixYt4t///d9ZuXJl1df1rW99iw9/+MNVX+6cOXOYOXMmN9xwQ9WX3SalRGtra+8zbgHyG8AehCVpK3LnnXdSW1vL5z73ufZp06dP56CDDuo035IlSzjooIM46KCDmDFjBn/+858BWLFiBQcffDDTp09nypQp3HPPPRQKBWbPns2UKVOYOnUqF198MQCzZ8/muuuuA+D+++/ngAMOoL6+nv32249169a1r2PGjBmd1rE5Tz/9NOvXr+ff/u3fmDNnTvv09evXc9pppzF16lSmTZvG9ddfD8Btt93GjBkzqK+vZ9as4iUnzj//fC666KL2106ZMoUlS5awZMkS9t57bz7/+c8zY8YMnn/+ef7pn/6JhoYGJk+ezHnnndf+mu6256CDDmLBggXt8xx44IEsXLiwrPelP/Lbf+s+YEkZ+d///SiLXlhb1WW+9+1jOe+oyT0+/8gjj7Dvvvv2upxddtmF22+/nebmZl588UVOOukkHnjgAX7xi19w+OGH841vfINCocAbb7zBggULWL58OY888ggAa9as6bSsN998k0984hNce+21zJw5k7Vr17Ltttu2r2PEiBE8+eST7evYnDlz5nDSSSdx0EEH8fjjj/PSSy+xyy678O1vf5vtttuOhx9+GIDVq1fz8ssvc8YZZ3D33Xez22678eqrr/a63Y8//jiXX345//Vf/wXABRdcwLhx4ygUCsyaNYuFCxey1157dbs9p59+OldccQXf/e53eeKJJ9i4cSPTpk3rdZ39ld8WsOcBS9JbNDc3c8YZZ7D//vtzwgknsGjRIgBmzpzJ5Zdfzvnnn8/DDz/MmDFj2H333XnmmWc466yzuO222xg7dmynZT3++ONMmDCBmTNnAjB27FiGDx/evo6pU6d2WsfmzJ07lxNPPJGamho+9rGP8ctf/hKA3//+93zhC19on2+HHXbg3nvv5eCDD24/pWfcuHG9Ln/XXXdl//33b388b948ZsyYwT777MOjjz7KokWLetyeE044gZtvvpnm5mYuu+wyZs+e3ev6qiG/6WUXtKSMbK6lOlAmT57c3i28ORdffDHjx4/nz3/+M6NGjWLEiBEAHHzwwdx9993ccsstnHzyyXzlK1/hlFNO4aGHHuK3v/0tP/zhD5k3bx6XXXZZ+7JSSt0e3du2joceeojW1tb2dfRk4cKFPPnkkxx66KFAsWW9++6784UvfKHbdfS03uHDh3fav9vxtJ9Ro0a133/22We56KKLuP/++9lhhx2YPXs2GzZs6HG5I0eO5NBDD+XGG29k3rx5vbbmqyXHLeDSpSg9CEvSVuBDH/oQGzdu5Cc/+Un7tPvvv5+77rqr03yvvfYaEyZMoKamhquvvppCobi7bunSpeyyyy6cccYZfPazn+XBBx/klVdeobW1leOPP55vf/vbPPjgg52Wtddee/HCCy9w//33A8WLUrS0tPS4jp7MmTOH888/v31/7QsvvMDy5ctZunQphx12GD/4wQ/a5129ejXvf//7ueuuu3j22WcB2rugJ02a1F7GBx98sP35rtauXcuoUaPYbrvtWLlyJbfeeutmtwfg9NNP5+yzz2bmzJlltbirIb8B7GlIkrYiEcENN9zA7bffzh577MHkyZM5//zzefvb395pvs9//vNceeWVfOhDH+KJJ55obxk2NTUxffp09tlnH66//nrOOeccli9fTmNjI9OnT2f27Nn8x3/8R6dl1dXVce2113LWWWdRX1/PoYceyoYNG9rXsf/++3daR0/mzp3Lcccd12nacccdx9y5c/nXf/1XVq9ezZQpU6ivr+fOO+9k55135tJLL+VjH/sY9fX1fOITnwDg+OOP59VXX2X69On86Ec/4t3vfne366uvr2efffZh8uTJfOYzn+HAAw/c7PYA7LvvvowdO5bTTjutzHek/yKlNGgra2hoSFVr2i/+b7j20/A/74EJA7+zfChz4O7qsB6rY0utx8WLF7P33ntnXYyyeSnKyrzwwgs0Njby2GOPUVOzqW1aST129xmJiPkppYbu5s9vC9jzgCVJVXDVVVfxvve9jwsuuKBT+A60/PbfOhyhJKkKTjnlFE455ZRBX+8QaAHn9zeEJGnrld8AtgUsScqxHAewR0FLkvIrvwHsecCSpBzLbwDbApa0lYkITj755PbHLS0t7Lzzznz0ox/t9bWjR48GioM1/OIXv2if/sADD3D22Wd3+5rGxsZerwo1adIkXnnllXKKD8AVV1zBP//zP/f4/DHHHMP73//+speXZ/kNYE9DkrSVGTVqFI888gh///vfAbj99tt5xzveUdEyugZwQ0MDl1xySVXL2Vdr1qzhwQcfZM2aNT1e5aoa2q5+lbX8BrCjIUnaCh1xxBHccsstwKYRhtr0NFxfR+eeey733HMP06dP5+KLL6apqamsFnRPw/sB/Od//if77bcf++23H0899RQAL7/8MscffzwzZ85k5syZ/OlPf+p1Hddffz1HHXUUJ554InPnzm2f/tRTT/HhD3+Y+vp6ZsyYwdNPPw3Ad77zHaZOnUp9fT3nnnsu0LnV/sorrzBp0iSg2PI+4YQTOOqoozjssMNYv349s2bNYsaMGUydOpUbb7yxfX1XXXUV06ZNo76+njPOOIN169ax22670dxcbPitXbuWSZMmtT/uq/yml13QkrJy67nw4sPVXebbpsIRF/Y624knnsi3vvUtPvrRj7Jw4UI+85nPcM8995S9mgsvvJCLLrqIm2++GSheeawc3Q3v1zZk39ixY/nrX//KVVddxRe/+EVuvvlmzjnnHL70pS/xgQ98gOeee47DDz+cxYsXb3Ydc+bM4bzzzmP8+PF8/OMf52tf+xoAn/rUpzj33HM57rjj2LBhA62trdx66638+te/5r777mPkyJFlDVn4l7/8hYULFzJu3DhaWlq44YYbGDt2LK+88gr7778/Rx99NIsWLeKCCy7gT3/6EzvttBNLly5lzJgxNDY2csstt3Dssccyd+5cjj/+eGpr+9cDm9/02mESr+6wD+O6GdlCkoaqadOmsWTJEubMmcORRx45aOudN28el156KS0tLaxYsYJFixa1B3BbK/ykk07iS1/6ElAcZrDjMIVr165l3bp1PS5/5cqVPPXUU3zgAx8gIhg+fDiPPPIIu+66K8uXL2+/lnTbyEu///3vOe200xg5ciRQ3pCFhx56aPt8KSW+/vWvc/fdd1NTU8Py5ctZuXIlf/jDH/j4xz/OTjvt1Gm5p59+Ot/5znc49thjufzyyzsNitFX+Q3g6Z9k4Zq305h1OSRtfcpoqQ6ko48+mi9/+cs0NTWxatWq9umbG66vHIcffjgrV66koaGBn/70p+3Texrer03HIf7a7re2tvKXv/yFbbfdtqx1X3vttaxevbp9DOC1a9cyd+5cvvrVr3Y7fzlDFnbd/o6DRlxzzTW8/PLLzJ8/n9raWiZNmrTZIQsPPPBAlixZwl133UWhUGDKlCllbdfm5HcfsCRtpT7zmc/wzW9+k6lTp3aa3nG4vgULFnR7INOYMWN6bIn+9re/ZcGCBZ3CF3oe3q/Ntdde237bdgRz12EGFyxYsNltmjNnDrfddlv7kIXz589n7ty5jB07lokTJ/LrX/8agI0bN/LGG29w2GGHcdlll/HGG28AnYcsnD9/PsBmx09+7bXX2GWXXaitreXOO+9k6dKlAMyaNYt58+a1/7Dp2LV9yimncNJJJ1VtxCQDWJJyZuLEiZxzzjlvmd5xuL6f/exn3Q7XN23aNIYPH059fT0XX3xxWevraXi/Nhs3buR973sf3/ve99qXeckll/DAAw8wbdo03vve9/LjH/+4x+UvWbKE5557jv3337992m677cbYsWO57777uPrqq7nkkkuYNm0aBxxwAC+++CIf+chHOProo2loaGD69OntB599+ctf5kc/+hEHHHDAZk+P+tSnPsUDDzxAQ0MD11xzDXvttRcAkydP5hvf+AYf/OAHqa+v5+tf/3qn16xevbrTgW/9kd/hCNlyhy3LG+uxOqzH6thS69HhCLdOHevxuuuu48Ybb+Tqq6/udt5KhyPM7z5gSZIGyVlnncWtt97Kb37zm6ot0wCWJKkX3//+96u+TPcBS5KUAQNYkso0mMfMKF/68tkwgCWpDCNGjGDVqlWGsN4ipcSqVavaLxJSLvcBS1IZJk6cyLJly3j55ZezLkpZNmzYUHEg6K3KrccRI0YwceLEipZtAEtSGWpra9uv0pQHTU1N7LPPPlkXI/cGsh7tgpYkKQMGsCRJGTCAJUnKwKBeijIiXgaWVnGROwE9X+xT5bIeq8N6rA7rsTqsx+robz3umlLaubsnBjWAqy0iHujpGpsqn/VYHdZjdViP1WE9VsdA1qNd0JIkZcAAliQpA3kP4EuzLsAQYT1Wh/VYHdZjdViP1TFg9ZjrfcCSJOVV3lvAkiTlUi4COCI+EhGPR8RTEXFuN89HRFxSen5hRMzIopxbujLq8VOl+lsYEX+OiPosyrml660eO8w3MyIKEfHxwSxfXpRTjxHRGBELIuLRiLhrsMuYB2X8X28XEf8dEQ+V6vG0LMq5JYuIyyLipYh4pIfnByZjUkpb9B8wDHga2B2oAx4C3ttlniOBW4EA9gfuy7rcW9pfmfV4ALBD6f4R1mPf6rHDfH8AfgN8POtyb2l/ZX4etwcWAe8qPd4l63JvaX9l1uPXgf9Tur8z8CpQl3XZt6Q/4GBgBvBID88PSMbkoQW8H/BUSumZlNKbwFzgmC7zHANclYruBbaPiAmDXdAtXK/1mFL6c0ppdenhvUBlQ3tsHcr5PAKcBVwPvDSYhcuRcurxk8CvUkrPAaSUrMu3KqceEzAmIgIYTTGAWwa3mFu2lNLdFOulJwOSMXkI4HcAz3d4vKw0rdJ5tnaV1tFnKf7iU2e91mNEvAM4DvjxIJYrb8r5PL4b2CEimiJifkScMmily49y6vEHwN7AC8DDwDkppdbBKd6QMSAZk4fhCKObaV0P3S5nnq1d2XUUEYdQDOAPDGiJ8qmcevwu8L9SSoVio0PdKKcehwP7ArOAbYG/RMS9KaUnBrpwOVJOPR4OLAA+BOwB3B4R96SU1g5w2YaSAcmYPATwMuCdHR5PpPhLrtJ5tnZl1VFETAN+ChyRUlo1SGXLk3LqsQGYWwrfnYAjI6IlpfTrQSlhPpT7f/1KSul14PWIuBuoBwzgTcqpx9OAC1NxZ+ZTEfEssBfw18Ep4pAwIBmThy7o+4E9I2K3iKgDTgRu6jLPTcAppSPV9gdeSymtGOyCbuF6rceIeBfwK+BkWxk96rUeU0q7pZQmpZQmAdcBnzd836Kc/+sbgYMiYnhEjATeBywe5HJu6cqpx+co9iIQEeOB9wDPDGop829AMmaLbwGnlFoi4p+B31I84u+ylNKjEfG50vM/pnik6ZHAU8AbFH/xqYMy6/GbwI7Af5Vaby3Ji7l3UmY9qhfl1GNKaXFE3AYsBFqBn6aUuj1NZGtV5ufx28AVEfEwxa7U/5VScpSkDiJiDtAI7BQRy4DzgFoY2IzxSliSJGUgD13QkiQNOQawJEkZMIAlScqAASxJUgYMYEmSMmAASxmIiB1Lo/wsiIgXI2J56f6aiFg0AOs7PyK+3M30ST2NALOZZV3R3QhPpZGLbu5POaWtiQEsZSCltCqlND2lNJ3iNaMvLt2fTvGc182KiC3+HH5Jm2cAS1ueYRHxk9LYrb+LiG0BSoMS/HtpXNxzImLfiLirNFDBb9tGZ4mIsyNiUWnc0rkdlvve0jKeiYizy1jf9Ii4t7ScGyJih64FjeJYtI9FxB+Bjw1clUhDjwEsbXn2BH6YUpoMrAGO7/Dc9imlDwKXAN+nONbwvsBlwAWlec4F9kkpTQM+1+G1e1G8MP9+wHkRUdvL+q6ieNWkaRRH0TmvYyEjYgTwE+Ao4CDgbf3bbGnrYgBLW55nU0oLSvfnA5M6PHdt6fY9wBSKI9ssAP6VTeM3LwSuiYhP03nc11tSShtLlyF8CRjf0/oiYjuKYX9XafqVFAct72iv0mufLF3o/+d92FZpq+V+JGnLs7HD/QLFofjavF66DeDRlNL7u3n9P1AMy6OB/zciJvew3OE9TO+4vt54LVupj2wBS/n0OLBzRLwfICJqI2JyRNQA70wp3Ql8FdgeGF3pwlNKrwGrI+Kg0qSTgbu6zPYYsFtE7FF6fFLlmyFtvWwBSzmUUnqzdCrQJaXu4uHAdymOlfvz0rSgeHT1mtLoVpU6FfhxaSjAZ+gyAkxKaUNEnAncEhGvAH+k2C0uqQyOhiRJUgbsgpYkKQMGsCRJGTCAJUnKgAEsSVIGDGBJkjJgAEuSlAEDWJKkDBjAkiRl4P8HZYTaor8C0/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tests\n",
    "%run utils_MultiLabel_vs_Classical.py\n",
    "test_df_106 = get_results_df_Classical(test_dataset_106, test_bl_106, test_indices_106, model_Classical)\n",
    "Classical = []\n",
    "multiLabel = []\n",
    "I =[]\n",
    "for i in range(0,100,1):\n",
    "    test_df_15 = get_results_df_multiLabel(test_dataset_15, test_bl_15, test_indices_15, model_MultiLabel, threshhold = i/100)\n",
    "    I.append(i/100)\n",
    "    multiLabel.append(accuracy(test_df_15))\n",
    "    Classical.append(accuracy(test_df_106))\n",
    "    \n",
    "    \n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(I,Classical, label='Classical Accuracy')\n",
    "plt.plot(I,multiLabel, label='Multi-label Accuracy')\n",
    "plt.title(\"Models'Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.xlabel('Threshhold')\n",
    "plt.legend(loc='lower right') \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b835e04",
   "metadata": {},
   "source": [
    "## Accuracy according to speedup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5c35d9d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical 87.29609490855165 0.9674458018501518\n",
      "MultiLabel 66.6831438457736 0.2612809829814279\n"
     ]
    }
   ],
   "source": [
    "%run utils_MultiLabel_vs_Classical.py\n",
    "df = test_df_106\n",
    "ds = test_dataset_106\n",
    "\n",
    "Acc_speed, ill = accuracy_speedup_Classical(df,ds, 0.1)\n",
    "print(\"Classical\", Acc_speed, ill)\n",
    "\n",
    "df = test_df_15\n",
    "ds = test_dataset_15\n",
    "#ds = bench_ds_15\n",
    "\n",
    "Acc_speed, ill = accuracy_speedup_MultiLabel(df,ds, 0.1)\n",
    "print(\"MultiLabel\", Acc_speed, ill)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d78451",
   "metadata": {},
   "source": [
    "## Saving the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a49b7d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model_Classical.state_dict(), 'Models/Classical_MC.pkl')\n",
    "torch.save(model_MultiLabel.state_dict(), 'Models/MultiLabel_MC.pkl')"
   ]
  }
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